diff --git a/PAR 152/Yolo Tensorflow/README.md b/PAR 152/Yolo Tensorflow/README.md
deleted file mode 100644
index 8c8c3cfc9b318b6f9f02df02c39353c882f27334..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo Tensorflow/README.md	
+++ /dev/null
@@ -1,53 +0,0 @@
-# YOLOv3-TensorFlow-2.x
-YOLOv3 implementation in TensorFlow 2 version. My Blog post https://medium.com/analytics-vidhya/yolov3-object-detection-in-tensorflow-2-x-8a1a104c46a8
-
-## Installation
-
-First, clone or download this GitHub repository. Install requirements and download pretrained weights:
-
-```
-git clone https://github.com/anushkadhiman/YOLOv3-TensorFlow-2.x.git
-cd YOLOv3-TensorFlow-2.x
-````
-
-```
-pip install -r ./requirements.txt
-`````
-
-```
-# yolov3
-wget -P model_data https://pjreddie.com/media/files/yolov3.weights
-
-# yolov3-tiny
-wget -P model_data https://pjreddie.com/media/files/yolov3-tiny.weights
-``````
-
-
-## Detection
-Start with using pretrained weights to test predictions on both image and video:
-```
-python detection_demo.py
-````
-
-## Result
-
-### Detection by Pre-trained model
-![Alt text](detect.jpg?raw=true "image1")
-![Alt text](detect2.jpg?raw=true "image2")
-![Alt text](video.gif?raw=true "video")
-
-### Detection by Custom Trained model
-![Alt text](video2.gif?raw=true "video")
-
-## References
-
-1. https://github.com/pjreddie/darknet	
-- Official YOLOv3 implementation
-2. https://github.com/AlexeyAB	
-- Explanations of parameters
-3. https://github.com/pythonlessons/TensorFlow-2.x-YOLOv3 
-- Models, loss functions
-
-4. https://pylessons.com/ — Rokas Balsys
-
-
diff --git a/PAR 152/Yolo Tensorflow/checkpoints/checkpoint b/PAR 152/Yolo Tensorflow/checkpoints/checkpoint
deleted file mode 100644
index 6590b878cf7a0f8930015f17a619bf8744725c24..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo Tensorflow/checkpoints/checkpoint	
+++ /dev/null
@@ -1,2 +0,0 @@
-model_checkpoint_path: "yolov3_custom_2"
-all_model_checkpoint_paths: "yolov3_custom_2"
diff --git a/PAR 152/Yolo Tensorflow/detection_custom.py b/PAR 152/Yolo Tensorflow/detection_custom.py
deleted file mode 100644
index f965f954612c5c347d6a76b01034f5c83611b334..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo Tensorflow/detection_custom.py	
+++ /dev/null
@@ -1,21 +0,0 @@
-import os
-os.environ['CUDA_VISIBLE_DEVICES'] = '0'
-import cv2
-import numpy as np
-import tensorflow as tf
-from yolov3.utils import detect_image, detect_realtime, detect_video, Load_Yolo_model, detect_video_realtime_mp
-from yolov3.configs import *
-
-image_path   = "./IMAGES/image.jpg"
-video_path   = "./IMAGES/video.mp4"
-
-yolo = Load_Yolo_model()
-#detect_image(yolo, image_path, "./IMAGES/detect.jpg", input_size=YOLO_INPUT_SIZE, show=True, CLASSES=TRAIN_CLASSES, rectangle_colors=(255,0,0))
-#detect_video(yolo, video_path, './IMAGES/detected.mp4', input_size=YOLO_INPUT_SIZE, show=False, CLASSES=TRAIN_CLASSES, rectangle_colors=(255,0,0))
-detect_realtime(yolo, '', input_size=YOLO_INPUT_SIZE, show=True, CLASSES=TRAIN_CLASSES, rectangle_colors=(255, 0, 0))
-
-detect_realtime(yolo, '', input_size=YOLO_INPUT_SIZE, show=True, rectangle_colors=(255, 0, 0))
-
-
-
-#detect_video_realtime_mp(video_path, "Output.mp4", input_size=YOLO_INPUT_SIZE, show=True, CLASSES=TRAIN_CLASSES, rectangle_colors=(255,0,0), realtime=False)
diff --git a/PAR 152/Yolo Tensorflow/detection_demo.py b/PAR 152/Yolo Tensorflow/detection_demo.py
deleted file mode 100644
index 75e557d8939d2ee0155dda4c39a4a5cc2fbc3b82..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo Tensorflow/detection_demo.py	
+++ /dev/null
@@ -1,18 +0,0 @@
-import os
-os.environ['CUDA_VISIBLE_DEVICES'] = "0"
-import cv2
-import numpy as np
-import tensorflow as tf
-from yolov3.utils import detect_image, detect_realtime, detect_video, Load_Yolo_model, detect_video_realtime_mp
-from yolov3.configs import *
-
-image_path   = "image2.jpg"
-video_path   = "test.mp4"
-    
-
-yolo = Load_Yolo_model()
-#detect_image(yolo, image_path, "detect.jpg", input_size=YOLO_INPUT_SIZE, show=True, rectangle_colors=(255,0,0))
-#detect_video(yolo, video_path, "", input_size=YOLO_INPUT_SIZE, show=False, rectangle_colors=(255,0,0))
-detect_realtime(yolo, '', input_size=YOLO_INPUT_SIZE, show=True, rectangle_colors=(255, 0, 0))
-
-#detect_video_realtime_mp(video_path, "Output.mp4", input_size=YOLO_INPUT_SIZE, show=False, rectangle_colors=(255,0,0), realtime=False)
diff --git a/PAR 152/Yolo Tensorflow/image.jpg b/PAR 152/Yolo Tensorflow/image.jpg
deleted file mode 100644
index 833cc7a147ab4f700dbdfb13c8bffeb05c74d5c8..0000000000000000000000000000000000000000
Binary files a/PAR 152/Yolo Tensorflow/image.jpg and /dev/null differ
diff --git a/PAR 152/Yolo Tensorflow/image2.jpg b/PAR 152/Yolo Tensorflow/image2.jpg
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index bd97194e3f79c68c0f16ea3952db1ce875886894..0000000000000000000000000000000000000000
Binary files a/PAR 152/Yolo Tensorflow/image2.jpg and /dev/null differ
diff --git a/PAR 152/Yolo Tensorflow/model_data/coco/coco.names b/PAR 152/Yolo Tensorflow/model_data/coco/coco.names
deleted file mode 100644
index eea3a0b36c2fc9c31f635c0df11de15048c0dfa7..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo Tensorflow/model_data/coco/coco.names	
+++ /dev/null
@@ -1,80 +0,0 @@
-person
-bicycle
-car
-motorbike
-aeroplane
-bus
-train
-truck
-boat
-traffic-light
-fire-hydrant
-stop-sign
-parking-meter
-bench
-bird
-cat
-dog
-horse
-sheep
-cow
-elephant
-bear
-zebra
-giraffe
-backpack
-umbrella
-handbag
-tie
-suitcase
-frisbee
-skis
-snowboard
-sports-ball
-kite
-baseball-bat
-baseball-glove
-skateboard
-surfboard
-tennis-racket
-bottle
-wine-glass
-cup
-fork
-knife
-spoon
-bowl
-banana
-apple
-sandwich
-orange
-broccoli
-carrot
-hot-dog
-pizza
-donut
-cake
-chair
-sofa
-pottedplant
-bed
-diningtable
-toilet
-tvmonitor
-laptop
-mouse
-remote
-keyboard
-cell-phone
-microwave
-oven
-toaster
-sink
-refrigerator
-book
-clock
-vase
-scissors
-teddy-bear
-hair-drier
-toothbrush
diff --git a/PAR 152/Yolo Tensorflow/model_data/coco/val2017.txt b/PAR 152/Yolo Tensorflow/model_data/coco/val2017.txt
deleted file mode 100644
index e341466a7f142854bf3dc4a0623c0908f129ef3a..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo Tensorflow/model_data/coco/val2017.txt	
+++ /dev/null
@@ -1,4952 +0,0 @@
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diff --git a/PAR 152/Yolo Tensorflow/test.mp4 b/PAR 152/Yolo Tensorflow/test.mp4
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diff --git a/PAR 152/Yolo Tensorflow/yolov3/configs.py b/PAR 152/Yolo Tensorflow/yolov3/configs.py
deleted file mode 100644
index 20d95e9e511f3ec4cc2f8163840f0c8ca5c1bed9..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo Tensorflow/yolov3/configs.py	
+++ /dev/null
@@ -1,53 +0,0 @@
-# YOLO options
-YOLO_TYPE                   = "yolov3"
-YOLO_FRAMEWORK              = "tf" # "tf" or "trt"
-YOLO_V3_WEIGHTS             = "model_data/yolov3.weights"
-YOLO_V3_TINY_WEIGHTS        = "model_data/yolov3-tiny.weights"
-YOLO_TRT_QUANTIZE_MODE      = "INT8" # INT8, FP16, FP32
-YOLO_CUSTOM_WEIGHTS         = False # "checkpoints/yolov3_custom" # used in evaluate_mAP.py and custom model detection, if not using leave False
-                            # YOLO_CUSTOM_WEIGHTS also used with TensorRT and custom model detection
-YOLO_COCO_CLASSES           = "model_data/coco/coco.names"
-YOLO_STRIDES                = [8, 16, 32]
-YOLO_IOU_LOSS_THRESH        = 0.5
-YOLO_ANCHOR_PER_SCALE       = 3
-YOLO_MAX_BBOX_PER_SCALE     = 100
-YOLO_INPUT_SIZE             = 416
-YOLO_ANCHORS            = [[[10,  13], [16,   30], [33,   23]],
-                           [[30,  61], [62,   45], [59,  119]],
-                           [[116, 90], [156, 198], [373, 326]]]
-# Train options
-TRAIN_YOLO_TINY             = False
-TRAIN_SAVE_BEST_ONLY        = True # saves only best model according validation loss (True recommended)
-TRAIN_SAVE_CHECKPOINT       = False # saves all best validated checkpoints in training process (may require a lot disk space) (False recommended)
-TRAIN_CLASSES               = "model_data/custom_data.names"
-TRAIN_ANNOT_PATH            = "model_data/custom_data_train.txt"
-TRAIN_LOGDIR                = "log"
-TRAIN_CHECKPOINTS_FOLDER    = "checkpoints"
-TRAIN_MODEL_NAME            = f"{YOLO_TYPE}_custom"
-TRAIN_LOAD_IMAGES_TO_RAM    = True # With True faster training, but need more RAM
-TRAIN_BATCH_SIZE            = 4
-TRAIN_INPUT_SIZE            = 416
-TRAIN_DATA_AUG              = True
-TRAIN_TRANSFER              = True
-TRAIN_FROM_CHECKPOINT       = False # "checkpoints/yolov3_custom"
-TRAIN_LR_INIT               = 1e-4
-TRAIN_LR_END                = 1e-6
-TRAIN_WARMUP_EPOCHS         = 2
-TRAIN_EPOCHS                = 100
-
-# TEST options
-TEST_ANNOT_PATH             = "model_data/custom_data_test.txt"
-TEST_BATCH_SIZE             = 4
-TEST_INPUT_SIZE             = 416
-TEST_DATA_AUG               = False
-TEST_DECTECTED_IMAGE_PATH   = ""
-TEST_SCORE_THRESHOLD        = 0.3
-TEST_IOU_THRESHOLD          = 0.45
-
-
-#YOLOv3-TINY WORKAROUND
-if TRAIN_YOLO_TINY:
-    YOLO_STRIDES            = [16, 32, 64]    
-    YOLO_ANCHORS            = [[[10,  14], [23,   27], [37,   58]],
-                               [[81,  82], [135, 169], [344, 319]],
-                               [[0,    0], [0,     0], [0,     0]]]
diff --git a/PAR 152/Yolo Tensorflow/yolov3_detection.ipynb b/PAR 152/Yolo Tensorflow/yolov3_detection.ipynb
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--- a/PAR 152/Yolo Tensorflow/yolov3_detection.ipynb	
+++ /dev/null
@@ -1 +0,0 @@
-{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"yolov3_detection.ipynb","provenance":[],"mount_file_id":"1Gx2gYPm0v8mLGJ0KMzSp5fAheLmKS3E9","authorship_tag":"ABX9TyNuLd7Zxcw0PjcvH4xS0gMw"},"kernelspec":{"name":"python3","display_name":"Python 3"},"accelerator":"GPU"},"cells":[{"cell_type":"code","metadata":{"id":"K0w7AFxLg83A","executionInfo":{"status":"ok","timestamp":1603021651522,"user_tz":-330,"elapsed":2008,"user":{"displayName":"Anushka Dhiman","photoUrl":"","userId":"07819850712982590893"}},"outputId":"c1a3d8ea-469d-47b2-a885-1cf14d50d018","colab":{"base_uri":"https://localhost:8080/","height":119}},"source":["!git clone https://github.com/anushkadhiman/YOLOv3-TensorFlow-2.x.git"],"execution_count":2,"outputs":[{"output_type":"stream","text":["Cloning into 'YOLOv3-TensorFlow-2.x'...\n","remote: Enumerating objects: 47, done.\u001b[K\n","remote: Counting objects: 100% (47/47), done.\u001b[K\n","remote: Compressing objects: 100% (40/40), done.\u001b[K\n","remote: Total 47 (delta 10), reused 22 (delta 1), pack-reused 0\u001b[K\n","Unpacking objects: 100% (47/47), done.\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"T2WQNiX0hblI","executionInfo":{"status":"ok","timestamp":1603021688828,"user_tz":-330,"elapsed":977,"user":{"displayName":"Anushka Dhiman","photoUrl":"","userId":"07819850712982590893"}},"outputId":"d27a56d6-74dc-45b3-a0db-a5e9b0027682","colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["cd /content/YOLOv3-TensorFlow-2.x"],"execution_count":4,"outputs":[{"output_type":"stream","text":["/content/YOLOv3-TensorFlow-2.x\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"fBuEYwuKiZdO","executionInfo":{"status":"ok","timestamp":1603022537261,"user_tz":-330,"elapsed":136358,"user":{"displayName":"Anushka Dhiman","photoUrl":"","userId":"07819850712982590893"}},"outputId":"d44812b7-5d87-4c9b-fb1a-06ce4095380b","colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["!pip install -r ./requirements.txt"],"execution_count":6,"outputs":[{"output_type":"stream","text":["Requirement already satisfied: numpy>=1.18.2 in /usr/local/lib/python3.6/dist-packages (from -r ./requirements.txt (line 1)) (1.18.5)\n","Requirement already satisfied: scipy>=1.4.1 in /usr/local/lib/python3.6/dist-packages (from -r ./requirements.txt (line 2)) (1.4.1)\n","Collecting wget>=3.2\n","  Downloading https://files.pythonhosted.org/packages/47/6a/62e288da7bcda82b935ff0c6cfe542970f04e29c756b0e147251b2fb251f/wget-3.2.zip\n","Requirement already satisfied: seaborn>=0.10.0 in /usr/local/lib/python3.6/dist-packages (from -r ./requirements.txt (line 4)) (0.11.0)\n","Collecting tensorflow==2.3.1\n","\u001b[?25l  Downloading https://files.pythonhosted.org/packages/ad/ad/769c195c72ac72040635c66cd9ba7b0f4b4fc1ac67e59b99fa6988446c22/tensorflow-2.3.1-cp36-cp36m-manylinux2010_x86_64.whl (320.4MB)\n","\u001b[K     |████████████████████████████████| 320.4MB 53kB/s \n","\u001b[?25hCollecting tensorflow-gpu==2.3.1\n","\u001b[?25l  Downloading https://files.pythonhosted.org/packages/18/99/ac32fd13d56e40d4c3e6150030132519997c0bb1f06f448d970e81b177e5/tensorflow_gpu-2.3.1-cp36-cp36m-manylinux2010_x86_64.whl (320.4MB)\n","\u001b[K     |████████████████████████████████| 320.4MB 52kB/s \n","\u001b[?25hRequirement already satisfied: opencv-python==4.1.2.30 in /usr/local/lib/python3.6/dist-packages (from -r ./requirements.txt (line 7)) (4.1.2.30)\n","Collecting tqdm==4.43.0\n","\u001b[?25l  Downloading https://files.pythonhosted.org/packages/47/55/fd9170ba08a1a64a18a7f8a18f088037316f2a41be04d2fe6ece5a653e8f/tqdm-4.43.0-py2.py3-none-any.whl (59kB)\n","\u001b[K     |████████████████████████████████| 61kB 8.7MB/s \n","\u001b[?25hRequirement already satisfied: pandas in /usr/local/lib/python3.6/dist-packages (from -r ./requirements.txt (line 9)) (1.1.2)\n","Collecting awscli\n","\u001b[?25l  Downloading https://files.pythonhosted.org/packages/ef/7c/319d222915f7d44d37119af0ef0650ee1542fee6295b9d04e8011f3b9cc5/awscli-1.18.159-py2.py3-none-any.whl (3.4MB)\n","\u001b[K     |████████████████████████████████| 3.4MB 54.7MB/s \n","\u001b[?25hRequirement already satisfied: urllib3 in /usr/local/lib/python3.6/dist-packages (from -r ./requirements.txt (line 11)) (1.24.3)\n","Collecting mss\n","\u001b[?25l  Downloading https://files.pythonhosted.org/packages/98/fe/b658d2a62efcad506d680e988cce0ff7d3e09bef288d67be062753ecfd73/mss-6.0.0-py3-none-any.whl (76kB)\n","\u001b[K     |████████████████████████████████| 81kB 13.4MB/s \n","\u001b[?25hRequirement already satisfied: matplotlib>=2.2 in /usr/local/lib/python3.6/dist-packages (from seaborn>=0.10.0->-r ./requirements.txt (line 4)) (3.2.2)\n","Requirement already satisfied: tensorboard<3,>=2.3.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.3.1->-r ./requirements.txt (line 5)) (2.3.0)\n","Requirement already satisfied: tensorflow-estimator<2.4.0,>=2.3.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.3.1->-r ./requirements.txt (line 5)) (2.3.0)\n","Requirement already satisfied: astunparse==1.6.3 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.3.1->-r ./requirements.txt (line 5)) (1.6.3)\n","Requirement already satisfied: six>=1.12.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.3.1->-r ./requirements.txt (line 5)) (1.15.0)\n","Requirement already satisfied: wheel>=0.26 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.3.1->-r ./requirements.txt (line 5)) (0.35.1)\n","Requirement already satisfied: h5py<2.11.0,>=2.10.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.3.1->-r ./requirements.txt (line 5)) (2.10.0)\n","Requirement already satisfied: grpcio>=1.8.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.3.1->-r ./requirements.txt (line 5)) (1.32.0)\n","Requirement already satisfied: google-pasta>=0.1.8 in 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/usr/local/lib/python3.6/dist-packages (from tensorflow==2.3.1->-r ./requirements.txt (line 5)) (1.1.0)\n","Requirement already satisfied: absl-py>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.3.1->-r ./requirements.txt (line 5)) (0.10.0)\n","Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas->-r ./requirements.txt (line 9)) (2018.9)\n","Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.6/dist-packages (from pandas->-r ./requirements.txt (line 9)) (2.8.1)\n","Collecting rsa<=4.5.0,>=3.1.2; python_version != \"3.4\"\n","  Downloading https://files.pythonhosted.org/packages/26/f8/8127fdda0294f044121d20aac7785feb810e159098447967a6103dedfb96/rsa-4.5-py2.py3-none-any.whl\n","Collecting colorama<0.4.4,>=0.2.5; python_version != \"3.4\"\n","  Downloading https://files.pythonhosted.org/packages/c9/dc/45cdef1b4d119eb96316b3117e6d5708a08029992b2fee2c143c7a0a5cc5/colorama-0.4.3-py2.py3-none-any.whl\n","Requirement already satisfied: PyYAML<5.4,>=3.10; python_version != \"3.4\" in /usr/local/lib/python3.6/dist-packages (from awscli->-r ./requirements.txt (line 10)) (3.13)\n","Collecting docutils<0.16,>=0.10\n","\u001b[?25l  Downloading https://files.pythonhosted.org/packages/22/cd/a6aa959dca619918ccb55023b4cb151949c64d4d5d55b3f4ffd7eee0c6e8/docutils-0.15.2-py3-none-any.whl (547kB)\n","\u001b[K     |████████████████████████████████| 552kB 23.9MB/s \n","\u001b[?25hCollecting s3transfer<0.4.0,>=0.3.0\n","\u001b[?25l  Downloading https://files.pythonhosted.org/packages/69/79/e6afb3d8b0b4e96cefbdc690f741d7dd24547ff1f94240c997a26fa908d3/s3transfer-0.3.3-py2.py3-none-any.whl (69kB)\n","\u001b[K     |████████████████████████████████| 71kB 12.2MB/s \n","\u001b[?25hCollecting botocore==1.18.18\n","\u001b[?25l  Downloading 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setuptools>=41.0.0 in /usr/local/lib/python3.6/dist-packages (from tensorboard<3,>=2.3.0->tensorflow==2.3.1->-r ./requirements.txt (line 5)) (50.3.0)\n","Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.6/dist-packages (from tensorboard<3,>=2.3.0->tensorflow==2.3.1->-r ./requirements.txt (line 5)) (3.2.2)\n","Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in /usr/local/lib/python3.6/dist-packages (from tensorboard<3,>=2.3.0->tensorflow==2.3.1->-r ./requirements.txt (line 5)) (0.4.1)\n","Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.6/dist-packages (from tensorboard<3,>=2.3.0->tensorflow==2.3.1->-r ./requirements.txt (line 5)) (1.0.1)\n","Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in /usr/local/lib/python3.6/dist-packages (from tensorboard<3,>=2.3.0->tensorflow==2.3.1->-r ./requirements.txt (line 5)) (1.7.0)\n","Requirement already satisfied: requests<3,>=2.21.0 in 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python_version < \"3.8\" in /usr/local/lib/python3.6/dist-packages (from markdown>=2.6.8->tensorboard<3,>=2.3.0->tensorflow==2.3.1->-r ./requirements.txt (line 5)) (2.0.0)\n","Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard<3,>=2.3.0->tensorflow==2.3.1->-r ./requirements.txt (line 5)) (1.3.0)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tensorboard<3,>=2.3.0->tensorflow==2.3.1->-r ./requirements.txt (line 5)) (2020.6.20)\n","Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tensorboard<3,>=2.3.0->tensorflow==2.3.1->-r ./requirements.txt (line 5)) (3.0.4)\n","Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tensorboard<3,>=2.3.0->tensorflow==2.3.1->-r ./requirements.txt (line 5)) (2.10)\n","Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.6/dist-packages (from importlib-metadata; python_version < \"3.8\"->markdown>=2.6.8->tensorboard<3,>=2.3.0->tensorflow==2.3.1->-r ./requirements.txt (line 5)) (3.2.0)\n","Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.6/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard<3,>=2.3.0->tensorflow==2.3.1->-r ./requirements.txt (line 5)) (3.1.0)\n","Building wheels for collected packages: wget\n","  Building wheel for wget (setup.py) ... \u001b[?25l\u001b[?25hdone\n","  Created wheel for wget: filename=wget-3.2-cp36-none-any.whl size=9682 sha256=28df1b4926901098899501da7fb7882c20913bec3bb57d85f7e436ae29efdcf1\n","  Stored in directory: /root/.cache/pip/wheels/40/15/30/7d8f7cea2902b4db79e3fea550d7d7b85ecb27ef992b618f3f\n","Successfully built wget\n","\u001b[31mERROR: datascience 0.10.6 has requirement folium==0.2.1, but you'll have folium 0.8.3 which is incompatible.\u001b[0m\n","Installing collected packages: wget, tensorflow, tensorflow-gpu, tqdm, rsa, colorama, docutils, jmespath, botocore, s3transfer, awscli, mss\n","  Found existing installation: tensorflow 2.3.0\n","    Uninstalling tensorflow-2.3.0:\n","      Successfully uninstalled tensorflow-2.3.0\n","  Found existing installation: tqdm 4.41.1\n","    Uninstalling tqdm-4.41.1:\n","      Successfully uninstalled tqdm-4.41.1\n","  Found existing installation: rsa 4.6\n","    Uninstalling rsa-4.6:\n","      Successfully uninstalled rsa-4.6\n","  Found existing installation: docutils 0.16\n","    Uninstalling docutils-0.16:\n","      Successfully uninstalled docutils-0.16\n","Successfully installed awscli-1.18.159 botocore-1.18.18 colorama-0.4.3 docutils-0.15.2 jmespath-0.10.0 mss-6.0.0 rsa-4.5 s3transfer-0.3.3 tensorflow-2.3.1 tensorflow-gpu-2.3.1 tqdm-4.43.0 wget-3.2\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"arre0QSZjSdz","executionInfo":{"status":"ok","timestamp":1603022133224,"user_tz":-330,"elapsed":177853,"user":{"displayName":"Anushka Dhiman","photoUrl":"","userId":"07819850712982590893"}},"outputId":"b4edd5ce-97d9-4802-e2d0-b5d2de7fc01d","colab":{"base_uri":"https://localhost:8080/","height":204}},"source":["# yolov3\n","!wget -P model_data https://pjreddie.com/media/files/yolov3.weights"],"execution_count":5,"outputs":[{"output_type":"stream","text":["--2020-10-18 11:52:35--  https://pjreddie.com/media/files/yolov3.weights\n","Resolving pjreddie.com (pjreddie.com)... 128.208.4.108\n","Connecting to pjreddie.com (pjreddie.com)|128.208.4.108|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 248007048 (237M) [application/octet-stream]\n","Saving to: ‘model_data/yolov3.weights’\n","\n","yolov3.weights      100%[===================>] 236.52M  5.00MB/s    in 2m 56s  \n","\n","2020-10-18 11:55:31 (1.34 MB/s) - ‘model_data/yolov3.weights’ saved [248007048/248007048]\n","\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"OvJwMLfRjeBz","executionInfo":{"status":"ok","timestamp":1603023615887,"user_tz":-330,"elapsed":61479,"user":{"displayName":"Anushka Dhiman","photoUrl":"","userId":"07819850712982590893"}},"outputId":"366723b7-7873-4b67-ef4a-1507fbc5fe02","colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["! python detection_demo.py"],"execution_count":15,"outputs":[{"output_type":"stream","text":["2020-10-18 12:19:14.879826: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1\n","2020-10-18 12:19:16.132763: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcuda.so.1\n","2020-10-18 12:19:16.169474: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n","2020-10-18 12:19:16.170030: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1716] Found device 0 with properties: \n","pciBusID: 0000:00:04.0 name: Tesla T4 computeCapability: 7.5\n","coreClock: 1.59GHz coreCount: 40 deviceMemorySize: 14.73GiB deviceMemoryBandwidth: 298.08GiB/s\n","2020-10-18 12:19:16.170069: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1\n","2020-10-18 12:19:16.171606: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcublas.so.10\n","2020-10-18 12:19:16.173379: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcufft.so.10\n","2020-10-18 12:19:16.173825: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcurand.so.10\n","2020-10-18 12:19:16.175668: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusolver.so.10\n","2020-10-18 12:19:16.176584: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusparse.so.10\n","2020-10-18 12:19:16.180299: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudnn.so.7\n","2020-10-18 12:19:16.180414: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n","2020-10-18 12:19:16.180989: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n","2020-10-18 12:19:16.181472: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1858] Adding visible gpu devices: 0\n","GPUs [PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]\n","2020-10-18 12:19:16.196735: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN)to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n","To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n","2020-10-18 12:19:16.201657: I tensorflow/core/platform/profile_utils/cpu_utils.cc:104] CPU Frequency: 2200000000 Hz\n","2020-10-18 12:19:16.201841: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x2922d80 initialized for platform Host (this does not guarantee that XLA will be used). Devices:\n","2020-10-18 12:19:16.201869: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version\n","2020-10-18 12:19:16.306688: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n","2020-10-18 12:19:16.307314: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x2922f40 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n","2020-10-18 12:19:16.307342: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Tesla T4, Compute Capability 7.5\n","2020-10-18 12:19:16.307516: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n","2020-10-18 12:19:16.308043: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1716] Found device 0 with properties: \n","pciBusID: 0000:00:04.0 name: Tesla T4 computeCapability: 7.5\n","coreClock: 1.59GHz coreCount: 40 deviceMemorySize: 14.73GiB deviceMemoryBandwidth: 298.08GiB/s\n","2020-10-18 12:19:16.308094: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1\n","2020-10-18 12:19:16.308130: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcublas.so.10\n","2020-10-18 12:19:16.308152: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcufft.so.10\n","2020-10-18 12:19:16.308179: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcurand.so.10\n","2020-10-18 12:19:16.308203: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusolver.so.10\n","2020-10-18 12:19:16.308221: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusparse.so.10\n","2020-10-18 12:19:16.308240: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudnn.so.7\n","2020-10-18 12:19:16.308311: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n","2020-10-18 12:19:16.308864: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n","2020-10-18 12:19:16.309341: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1858] Adding visible gpu devices: 0\n","2020-10-18 12:19:16.309400: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1\n","2020-10-18 12:19:17.020096: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1257] Device interconnect StreamExecutor with strength 1 edge matrix:\n","2020-10-18 12:19:17.020164: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1263]      0 \n","2020-10-18 12:19:17.020181: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1276] 0:   N \n","2020-10-18 12:19:17.020386: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n","2020-10-18 12:19:17.021036: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n","2020-10-18 12:19:17.021574: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1402] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 13936 MB memory) -> physical GPU (device: 0, name: Tesla T4, pci bus id: 0000:00:04.0, compute capability: 7.5)\n","OpenCV: FFMPEG: tag 0x44495658/'XVID' is not supported with codec id 12 and format 'mp4 / MP4 (MPEG-4 Part 14)'\n","OpenCV: FFMPEG: fallback to use tag 0x7634706d/'mp4v'\n","2020-10-18 12:19:20.772402: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudnn.so.7\n","2020-10-18 12:19:22.433127: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcublas.so.10\n","Time: 3715.51ms, Detection FPS: 0.3, total FPS: 0.3\n","Time: 1888.72ms, Detection FPS: 0.5, total FPS: 0.5\n","Time: 1278.97ms, Detection FPS: 0.8, total FPS: 0.8\n","Time: 974.44ms, Detection FPS: 1.0, total FPS: 1.0\n","Time: 791.33ms, Detection FPS: 1.3, total FPS: 1.2\n","Time: 669.98ms, Detection FPS: 1.5, total FPS: 1.5\n","Time: 583.90ms, Detection FPS: 1.7, total FPS: 1.7\n","Time: 518.40ms, Detection FPS: 1.9, total FPS: 1.9\n","Time: 467.50ms, Detection FPS: 2.1, total FPS: 2.1\n","Time: 426.62ms, Detection FPS: 2.3, total FPS: 2.3\n","Time: 393.35ms, Detection FPS: 2.5, total FPS: 2.5\n","Time: 365.48ms, Detection FPS: 2.7, total FPS: 2.6\n","Time: 341.84ms, Detection FPS: 2.9, total FPS: 2.8\n","Time: 321.54ms, Detection FPS: 3.1, total FPS: 3.0\n","Time: 304.62ms, Detection FPS: 3.3, total FPS: 3.1\n","Time: 289.27ms, Detection FPS: 3.5, total FPS: 3.3\n","Time: 275.72ms, Detection FPS: 3.6, total FPS: 3.5\n","Time: 263.84ms, Detection FPS: 3.8, total FPS: 3.6\n","Time: 253.43ms, Detection FPS: 3.9, total FPS: 3.8\n","Time: 244.04ms, Detection FPS: 4.1, total FPS: 3.9\n","Time: 61.67ms, Detection FPS: 16.2, total FPS: 13.4\n","Time: 61.89ms, Detection FPS: 16.2, total FPS: 13.4\n","Time: 62.29ms, Detection FPS: 16.1, total FPS: 13.3\n","Time: 62.66ms, Detection FPS: 16.0, total FPS: 13.2\n","Time: 63.71ms, Detection FPS: 15.7, total FPS: 13.0\n","Time: 64.05ms, Detection FPS: 15.6, total FPS: 12.9\n","Time: 64.04ms, Detection FPS: 15.6, total FPS: 12.9\n","Time: 64.38ms, Detection FPS: 15.5, total FPS: 12.9\n","Time: 64.79ms, Detection FPS: 15.4, total FPS: 12.8\n","Time: 65.25ms, Detection FPS: 15.3, total FPS: 12.7\n","Time: 65.62ms, Detection FPS: 15.2, total FPS: 12.7\n","Time: 66.09ms, Detection FPS: 15.1, total FPS: 12.6\n","Time: 66.52ms, Detection FPS: 15.0, total FPS: 12.5\n","Time: 67.28ms, Detection FPS: 14.9, total FPS: 12.4\n","Time: 67.41ms, Detection FPS: 14.8, total FPS: 12.4\n","Time: 68.17ms, Detection FPS: 14.7, total FPS: 12.3\n","Time: 68.64ms, Detection FPS: 14.6, total FPS: 12.2\n","Time: 69.16ms, Detection FPS: 14.5, total FPS: 12.2\n","Time: 69.40ms, Detection FPS: 14.4, total FPS: 12.1\n","Time: 69.63ms, Detection FPS: 14.4, total FPS: 12.1\n","Time: 69.69ms, Detection FPS: 14.3, total FPS: 12.1\n","Time: 69.97ms, Detection FPS: 14.3, total FPS: 12.1\n","Time: 70.37ms, Detection FPS: 14.2, total FPS: 12.0\n","Time: 70.46ms, Detection FPS: 14.2, total FPS: 12.0\n","Time: 70.48ms, Detection FPS: 14.2, total FPS: 12.0\n","Time: 70.57ms, Detection FPS: 14.2, total FPS: 12.0\n","Time: 70.73ms, Detection FPS: 14.1, total FPS: 12.0\n","Time: 71.00ms, Detection FPS: 14.1, total FPS: 12.0\n","Time: 71.21ms, Detection FPS: 14.0, total FPS: 12.0\n","Time: 71.58ms, Detection FPS: 14.0, total FPS: 11.9\n","Time: 71.72ms, Detection FPS: 13.9, total FPS: 11.9\n","Time: 72.09ms, Detection FPS: 13.9, total FPS: 11.8\n","Time: 72.36ms, Detection FPS: 13.8, total FPS: 11.8\n","Time: 72.57ms, Detection FPS: 13.8, total FPS: 11.7\n","Time: 72.71ms, Detection FPS: 13.8, total FPS: 11.7\n","Time: 72.75ms, Detection FPS: 13.7, total FPS: 11.7\n","Time: 73.00ms, Detection FPS: 13.7, total FPS: 11.7\n","Time: 73.05ms, Detection FPS: 13.7, total FPS: 11.7\n","Time: 73.17ms, Detection FPS: 13.7, total FPS: 11.7\n","Time: 73.30ms, Detection FPS: 13.6, total FPS: 11.7\n","Time: 73.70ms, Detection FPS: 13.6, total FPS: 11.6\n","Time: 73.83ms, Detection FPS: 13.5, total FPS: 11.6\n","Time: 73.92ms, Detection FPS: 13.5, total FPS: 11.6\n","Time: 74.03ms, Detection FPS: 13.5, total FPS: 11.6\n","Time: 73.68ms, Detection FPS: 13.6, total FPS: 11.6\n","Time: 73.93ms, Detection FPS: 13.5, total FPS: 11.6\n","Time: 74.02ms, Detection FPS: 13.5, total FPS: 11.5\n","Time: 74.07ms, Detection FPS: 13.5, total FPS: 11.5\n","Time: 74.13ms, Detection FPS: 13.5, total FPS: 11.5\n","Time: 74.54ms, Detection FPS: 13.4, total FPS: 11.5\n","Time: 74.66ms, Detection FPS: 13.4, total FPS: 11.4\n","Time: 74.77ms, Detection FPS: 13.4, total FPS: 11.4\n","Time: 74.90ms, Detection FPS: 13.4, total FPS: 11.4\n","Time: 75.08ms, Detection FPS: 13.3, total FPS: 11.4\n","Time: 75.27ms, Detection FPS: 13.3, total FPS: 11.3\n","Time: 75.53ms, Detection FPS: 13.2, total FPS: 11.3\n","Time: 75.82ms, Detection FPS: 13.2, total FPS: 11.2\n","Time: 76.23ms, Detection FPS: 13.1, total FPS: 11.2\n","Time: 76.71ms, Detection FPS: 13.0, total FPS: 11.1\n","Time: 76.84ms, Detection FPS: 13.0, total FPS: 11.1\n","Time: 76.67ms, Detection FPS: 13.0, total FPS: 11.1\n","Time: 76.88ms, Detection FPS: 13.0, total FPS: 11.1\n","Time: 76.70ms, Detection FPS: 13.0, total FPS: 11.2\n","Time: 76.75ms, Detection FPS: 13.0, total FPS: 11.1\n","Time: 76.77ms, Detection FPS: 13.0, total FPS: 11.1\n","Time: 76.69ms, Detection FPS: 13.0, total FPS: 11.1\n","Time: 76.84ms, Detection FPS: 13.0, total FPS: 11.1\n","Time: 77.32ms, Detection FPS: 12.9, total FPS: 11.1\n","Time: 77.79ms, Detection FPS: 12.9, total FPS: 11.0\n","Time: 77.44ms, Detection FPS: 12.9, total FPS: 11.1\n","Time: 77.56ms, Detection FPS: 12.9, total FPS: 11.1\n","Time: 77.55ms, Detection FPS: 12.9, total FPS: 11.1\n","Time: 77.44ms, Detection FPS: 12.9, total FPS: 11.1\n","Time: 77.00ms, Detection FPS: 13.0, total FPS: 11.2\n","Time: 76.90ms, Detection FPS: 13.0, total FPS: 11.2\n","Time: 76.56ms, Detection FPS: 13.1, total FPS: 11.2\n","Time: 76.57ms, Detection FPS: 13.1, total FPS: 11.2\n","Time: 76.28ms, Detection FPS: 13.1, total FPS: 11.3\n","Time: 75.79ms, Detection FPS: 13.2, total FPS: 11.3\n","Time: 75.71ms, Detection FPS: 13.2, total FPS: 11.3\n","Time: 75.64ms, Detection FPS: 13.2, total FPS: 11.4\n","Time: 75.40ms, Detection FPS: 13.3, total FPS: 11.4\n","Time: 75.49ms, Detection FPS: 13.2, total FPS: 11.4\n","Time: 75.56ms, Detection FPS: 13.2, total FPS: 11.4\n","Time: 75.52ms, Detection FPS: 13.2, total FPS: 11.4\n","Time: 75.85ms, Detection FPS: 13.2, total FPS: 11.3\n","Time: 75.83ms, Detection FPS: 13.2, total FPS: 11.3\n","Time: 75.39ms, Detection FPS: 13.3, total FPS: 11.4\n","Time: 74.92ms, Detection FPS: 13.3, total FPS: 11.5\n","Time: 74.93ms, Detection FPS: 13.3, total FPS: 11.5\n","Time: 74.78ms, Detection FPS: 13.4, total FPS: 11.5\n","Time: 74.63ms, Detection FPS: 13.4, total FPS: 11.5\n","Time: 74.68ms, Detection FPS: 13.4, total FPS: 11.5\n","Time: 74.73ms, Detection FPS: 13.4, total FPS: 11.5\n","Time: 75.02ms, Detection FPS: 13.3, total FPS: 11.4\n","Time: 75.26ms, Detection FPS: 13.3, total FPS: 11.4\n","Time: 75.06ms, Detection FPS: 13.3, total FPS: 11.4\n","Time: 74.93ms, Detection FPS: 13.3, total FPS: 11.4\n","Time: 75.01ms, Detection FPS: 13.3, total FPS: 11.4\n","Time: 74.94ms, Detection FPS: 13.3, total FPS: 11.4\n","Time: 74.95ms, Detection FPS: 13.3, total FPS: 11.4\n","Time: 75.01ms, Detection FPS: 13.3, total FPS: 11.4\n","Time: 75.02ms, Detection FPS: 13.3, total FPS: 11.4\n","Time: 75.51ms, Detection FPS: 13.2, total FPS: 11.4\n","Time: 75.78ms, Detection FPS: 13.2, total FPS: 11.3\n","Time: 75.46ms, Detection FPS: 13.3, total FPS: 11.4\n","Time: 75.38ms, Detection FPS: 13.3, total FPS: 11.4\n","Time: 75.48ms, Detection FPS: 13.2, total FPS: 11.4\n","Time: 75.39ms, Detection FPS: 13.3, total FPS: 11.4\n","Time: 75.18ms, Detection FPS: 13.3, total FPS: 11.4\n","Time: 75.28ms, Detection FPS: 13.3, total FPS: 11.4\n","Time: 75.21ms, Detection FPS: 13.3, total FPS: 11.4\n","Time: 75.70ms, Detection FPS: 13.2, total FPS: 11.4\n","Time: 75.72ms, Detection FPS: 13.2, total FPS: 11.4\n","Time: 75.37ms, Detection FPS: 13.3, total FPS: 11.4\n","Time: 75.10ms, Detection FPS: 13.3, total FPS: 11.5\n","Time: 75.23ms, Detection FPS: 13.3, total FPS: 11.4\n","Time: 75.48ms, Detection FPS: 13.2, total FPS: 11.4\n","Time: 75.47ms, Detection FPS: 13.3, total FPS: 11.4\n","Time: 75.48ms, Detection FPS: 13.2, total FPS: 11.4\n","Time: 75.81ms, Detection FPS: 13.2, total FPS: 11.3\n","Time: 76.35ms, Detection FPS: 13.1, total FPS: 11.3\n","Time: 76.42ms, Detection FPS: 13.1, total FPS: 11.2\n","Time: 76.16ms, Detection FPS: 13.1, total FPS: 11.3\n","Time: 75.94ms, Detection FPS: 13.2, total FPS: 11.3\n","Time: 75.91ms, Detection FPS: 13.2, total FPS: 11.3\n","Time: 75.88ms, Detection FPS: 13.2, total FPS: 11.3\n","Time: 75.74ms, Detection FPS: 13.2, total FPS: 11.3\n","Time: 75.81ms, Detection FPS: 13.2, total FPS: 11.3\n","Time: 75.98ms, Detection FPS: 13.2, total FPS: 11.3\n","Time: 76.36ms, Detection FPS: 13.1, total FPS: 11.2\n","Time: 81.62ms, Detection FPS: 12.3, total FPS: 10.6\n","Time: 81.25ms, Detection FPS: 12.3, total FPS: 10.6\n","Time: 81.25ms, Detection FPS: 12.3, total FPS: 10.6\n","Time: 81.17ms, Detection FPS: 12.3, total FPS: 10.6\n","Time: 81.21ms, Detection FPS: 12.3, total FPS: 10.6\n","Time: 81.04ms, Detection FPS: 12.3, total FPS: 10.7\n","Time: 80.96ms, Detection FPS: 12.4, total FPS: 10.7\n","Time: 81.12ms, Detection FPS: 12.3, total FPS: 10.7\n","Time: 81.62ms, Detection FPS: 12.3, total FPS: 10.6\n","Time: 81.28ms, Detection FPS: 12.3, total FPS: 10.6\n","Time: 80.84ms, Detection FPS: 12.4, total FPS: 10.7\n","Time: 80.59ms, Detection FPS: 12.4, total FPS: 10.7\n","Time: 80.24ms, Detection FPS: 12.5, total FPS: 10.8\n","Time: 80.18ms, Detection FPS: 12.5, total FPS: 10.8\n","Time: 80.16ms, Detection FPS: 12.5, total FPS: 10.8\n","Time: 80.35ms, Detection FPS: 12.4, total FPS: 10.8\n","Time: 80.35ms, Detection FPS: 12.4, total FPS: 10.8\n","Time: 80.79ms, Detection FPS: 12.4, total FPS: 10.7\n","Time: 80.61ms, Detection FPS: 12.4, total FPS: 10.7\n","Time: 80.14ms, Detection FPS: 12.5, total FPS: 10.8\n","Time: 74.98ms, Detection FPS: 13.3, total FPS: 11.4\n","Time: 75.00ms, Detection FPS: 13.3, total FPS: 11.4\n","Time: 75.12ms, Detection FPS: 13.3, total FPS: 11.4\n","Time: 75.43ms, Detection FPS: 13.3, total FPS: 11.4\n","Time: 75.64ms, Detection FPS: 13.2, total FPS: 11.3\n","Time: 75.88ms, Detection FPS: 13.2, total FPS: 11.3\n","Time: 76.10ms, Detection FPS: 13.1, total FPS: 11.3\n","Time: 75.98ms, Detection FPS: 13.2, total FPS: 11.3\n","Time: 75.69ms, Detection FPS: 13.2, total FPS: 11.3\n","Time: 75.82ms, Detection FPS: 13.2, total FPS: 11.3\n","Time: 75.90ms, Detection FPS: 13.2, total FPS: 11.3\n","Time: 76.09ms, Detection FPS: 13.1, total FPS: 11.3\n","Time: 76.23ms, Detection FPS: 13.1, total FPS: 11.3\n","Time: 76.90ms, Detection FPS: 13.0, total FPS: 11.2\n","Time: 77.07ms, Detection FPS: 13.0, total FPS: 11.1\n","Time: 77.43ms, Detection FPS: 12.9, total FPS: 11.1\n","Time: 77.59ms, Detection FPS: 12.9, total FPS: 11.1\n","Time: 77.51ms, Detection FPS: 12.9, total FPS: 11.1\n","Time: 77.63ms, Detection FPS: 12.9, total FPS: 11.1\n","Time: 77.90ms, Detection FPS: 12.8, total FPS: 11.0\n","Time: 77.91ms, Detection FPS: 12.8, total FPS: 11.0\n","Time: 77.91ms, Detection FPS: 12.8, total FPS: 11.0\n","Time: 77.76ms, Detection FPS: 12.9, total FPS: 11.1\n","Time: 77.65ms, Detection FPS: 12.9, total FPS: 11.1\n","Time: 77.69ms, Detection FPS: 12.9, total FPS: 11.1\n","Time: 77.35ms, Detection FPS: 12.9, total FPS: 11.1\n","Time: 76.91ms, Detection FPS: 13.0, total FPS: 11.2\n","Time: 76.97ms, Detection FPS: 13.0, total FPS: 11.2\n","Time: 77.10ms, Detection FPS: 13.0, total FPS: 11.2\n","Time: 76.95ms, Detection FPS: 13.0, total FPS: 11.2\n","Time: 76.61ms, Detection FPS: 13.1, total FPS: 11.3\n","Time: 76.60ms, Detection FPS: 13.1, total FPS: 11.2\n","Time: 76.52ms, Detection FPS: 13.1, total FPS: 11.3\n","Time: 76.20ms, Detection FPS: 13.1, total FPS: 11.3\n","Time: 75.97ms, Detection FPS: 13.2, total FPS: 11.4\n","Time: 75.49ms, Detection FPS: 13.2, total FPS: 11.4\n","Time: 75.52ms, Detection FPS: 13.2, total FPS: 11.4\n","Time: 75.19ms, Detection FPS: 13.3, total FPS: 11.4\n","Time: 75.08ms, Detection FPS: 13.3, total FPS: 11.4\n","Time: 75.00ms, Detection FPS: 13.3, total FPS: 11.4\n","Time: 74.98ms, Detection FPS: 13.3, total FPS: 11.4\n","Time: 74.78ms, Detection FPS: 13.4, total FPS: 11.5\n","Time: 75.04ms, Detection FPS: 13.3, total FPS: 11.4\n","Time: 75.07ms, Detection FPS: 13.3, total FPS: 11.4\n","Time: 74.78ms, Detection FPS: 13.4, total FPS: 11.5\n","Time: 74.77ms, Detection FPS: 13.4, total FPS: 11.5\n","Time: 74.91ms, Detection FPS: 13.3, total FPS: 11.5\n","Time: 74.80ms, Detection FPS: 13.4, total FPS: 11.5\n","Time: 74.67ms, Detection FPS: 13.4, total FPS: 11.5\n","Time: 74.68ms, Detection FPS: 13.4, total FPS: 11.5\n","Time: 74.83ms, Detection FPS: 13.4, total FPS: 11.5\n","Time: 74.94ms, Detection FPS: 13.3, total FPS: 11.5\n","Time: 74.92ms, Detection FPS: 13.3, total FPS: 11.5\n","Time: 74.85ms, Detection FPS: 13.4, total FPS: 11.5\n","Time: 75.07ms, Detection FPS: 13.3, total FPS: 11.4\n","Time: 75.25ms, Detection FPS: 13.3, total FPS: 11.4\n","Time: 75.17ms, Detection FPS: 13.3, total FPS: 11.5\n","Time: 75.41ms, Detection FPS: 13.3, total FPS: 11.4\n","Time: 75.58ms, Detection FPS: 13.2, total FPS: 11.4\n","Time: 75.53ms, Detection FPS: 13.2, total FPS: 11.4\n","Time: 75.93ms, Detection FPS: 13.2, total FPS: 11.4\n","Time: 76.26ms, Detection FPS: 13.1, total FPS: 11.3\n","Time: 76.15ms, Detection FPS: 13.1, total FPS: 11.3\n","Time: 76.05ms, Detection FPS: 13.1, total FPS: 11.3\n","Time: 76.19ms, Detection FPS: 13.1, total FPS: 11.3\n","Time: 76.26ms, Detection FPS: 13.1, total FPS: 11.3\n","Time: 76.27ms, Detection FPS: 13.1, total FPS: 11.3\n","Time: 76.21ms, Detection FPS: 13.1, total FPS: 11.3\n","Time: 76.05ms, Detection FPS: 13.1, total FPS: 11.3\n","Time: 76.32ms, Detection FPS: 13.1, total FPS: 11.3\n","Time: 76.25ms, Detection FPS: 13.1, total FPS: 11.3\n","Time: 75.97ms, Detection FPS: 13.2, total FPS: 11.4\n","Time: 76.08ms, Detection FPS: 13.1, total FPS: 11.3\n","Time: 75.79ms, Detection FPS: 13.2, total FPS: 11.4\n","Time: 75.53ms, Detection FPS: 13.2, total FPS: 11.4\n","Time: 75.47ms, Detection FPS: 13.3, total FPS: 11.4\n","Time: 75.40ms, Detection FPS: 13.3, total FPS: 11.4\n","Time: 75.12ms, Detection FPS: 13.3, total FPS: 11.5\n","Time: 75.32ms, Detection FPS: 13.3, total FPS: 11.5\n","Time: 75.17ms, Detection FPS: 13.3, total FPS: 11.5\n","Time: 74.64ms, Detection FPS: 13.4, total FPS: 11.6\n","Time: 74.43ms, Detection FPS: 13.4, total FPS: 11.6\n","Time: 74.46ms, Detection FPS: 13.4, total FPS: 11.6\n","Time: 74.64ms, Detection FPS: 13.4, total FPS: 11.6\n","Time: 74.94ms, Detection FPS: 13.3, total FPS: 11.5\n","Time: 75.12ms, Detection FPS: 13.3, total FPS: 11.5\n","Time: 75.23ms, Detection FPS: 13.3, total FPS: 11.5\n","Time: 75.90ms, Detection FPS: 13.2, total FPS: 11.4\n","Time: 76.22ms, Detection FPS: 13.1, total FPS: 11.4\n","Time: 76.14ms, Detection FPS: 13.1, total FPS: 11.4\n","Time: 76.40ms, Detection FPS: 13.1, total FPS: 11.3\n","Time: 76.38ms, Detection FPS: 13.1, total FPS: 11.3\n","Time: 76.26ms, Detection FPS: 13.1, total FPS: 11.3\n","Time: 76.26ms, Detection FPS: 13.1, total FPS: 11.3\n","Time: 76.24ms, Detection FPS: 13.1, total FPS: 11.3\n","Time: 76.00ms, Detection FPS: 13.2, total FPS: 11.4\n","Time: 76.21ms, Detection FPS: 13.1, total FPS: 11.4\n","Time: 76.28ms, Detection FPS: 13.1, total FPS: 11.3\n","Time: 75.99ms, Detection FPS: 13.2, total FPS: 11.4\n","Time: 75.99ms, Detection FPS: 13.2, total FPS: 11.4\n","Time: 76.20ms, Detection FPS: 13.1, total FPS: 11.4\n","Time: 76.26ms, Detection FPS: 13.1, total FPS: 11.3\n","Time: 76.04ms, Detection FPS: 13.2, total FPS: 11.4\n","Time: 75.77ms, Detection FPS: 13.2, total FPS: 11.4\n","Time: 75.41ms, Detection FPS: 13.3, total FPS: 11.5\n","Time: 75.46ms, Detection FPS: 13.3, total FPS: 11.5\n","Time: 75.29ms, Detection FPS: 13.3, total FPS: 11.5\n","Time: 74.57ms, Detection FPS: 13.4, total FPS: 11.6\n","Time: 74.37ms, Detection FPS: 13.4, total FPS: 11.6\n","Time: 74.42ms, Detection FPS: 13.4, total FPS: 11.6\n","Time: 74.37ms, Detection FPS: 13.4, total FPS: 11.6\n","Time: 74.52ms, Detection FPS: 13.4, total FPS: 11.6\n","Time: 74.68ms, Detection FPS: 13.4, total FPS: 11.5\n","Time: 74.66ms, Detection FPS: 13.4, total FPS: 11.5\n","Time: 75.00ms, Detection FPS: 13.3, total FPS: 11.5\n","Time: 75.03ms, Detection FPS: 13.3, total FPS: 11.5\n","Time: 74.90ms, Detection FPS: 13.4, total FPS: 11.5\n","Time: 74.81ms, Detection FPS: 13.4, total FPS: 11.5\n","Time: 74.89ms, Detection FPS: 13.4, total FPS: 11.5\n","Time: 75.09ms, Detection FPS: 13.3, total FPS: 11.5\n","Time: 74.86ms, Detection FPS: 13.4, total FPS: 11.5\n","Time: 74.86ms, Detection FPS: 13.4, total FPS: 11.5\n","Time: 74.94ms, Detection FPS: 13.3, total FPS: 11.5\n","Time: 75.21ms, Detection FPS: 13.3, total FPS: 11.4\n","Time: 75.12ms, Detection FPS: 13.3, total FPS: 11.5\n","Time: 75.08ms, Detection FPS: 13.3, total FPS: 11.5\n","Time: 75.29ms, Detection FPS: 13.3, total FPS: 11.4\n","Time: 75.26ms, Detection FPS: 13.3, total FPS: 11.5\n","Time: 75.16ms, Detection FPS: 13.3, total FPS: 11.5\n","Time: 75.08ms, Detection FPS: 13.3, total FPS: 11.5\n","Time: 75.05ms, Detection FPS: 13.3, total FPS: 11.5\n","Time: 74.99ms, Detection FPS: 13.3, total FPS: 11.5\n","Time: 75.22ms, Detection FPS: 13.3, total FPS: 11.4\n","Time: 75.25ms, Detection FPS: 13.3, total FPS: 11.4\n","Time: 75.03ms, Detection FPS: 13.3, total FPS: 11.4\n","Time: 75.42ms, Detection FPS: 13.3, total FPS: 11.4\n","Time: 75.75ms, Detection FPS: 13.2, total FPS: 11.4\n","Time: 75.98ms, Detection FPS: 13.2, total FPS: 11.3\n","Time: 75.96ms, Detection FPS: 13.2, total FPS: 11.3\n","Time: 75.79ms, Detection FPS: 13.2, total FPS: 11.3\n","Time: 75.92ms, Detection FPS: 13.2, total FPS: 11.3\n","Time: 76.19ms, Detection FPS: 13.1, total FPS: 11.3\n","Time: 76.41ms, Detection FPS: 13.1, total FPS: 11.3\n","Time: 76.32ms, Detection FPS: 13.1, total FPS: 11.3\n","Time: 76.50ms, Detection FPS: 13.1, total FPS: 11.2\n","Time: 76.55ms, Detection FPS: 13.1, total FPS: 11.2\n","Time: 76.48ms, Detection FPS: 13.1, total FPS: 11.2\n","Time: 76.77ms, Detection FPS: 13.0, total FPS: 11.2\n","Time: 76.76ms, Detection FPS: 13.0, total FPS: 11.2\n","Time: 77.00ms, Detection FPS: 13.0, total FPS: 11.2\n","Time: 77.24ms, Detection FPS: 12.9, total FPS: 11.1\n","Time: 77.32ms, Detection FPS: 12.9, total FPS: 11.1\n","Time: 77.07ms, Detection FPS: 13.0, total FPS: 11.2\n","Time: 77.20ms, Detection FPS: 13.0, total FPS: 11.2\n","Time: 77.16ms, Detection FPS: 13.0, total FPS: 11.1\n","Time: 76.79ms, Detection FPS: 13.0, total FPS: 11.2\n","Time: 76.33ms, Detection FPS: 13.1, total FPS: 11.2\n","Time: 76.06ms, Detection FPS: 13.1, total FPS: 11.3\n","Time: 75.89ms, Detection FPS: 13.2, total FPS: 11.3\n","Time: 76.35ms, Detection FPS: 13.1, total FPS: 11.2\n","Time: 76.37ms, Detection FPS: 13.1, total FPS: 11.2\n","Time: 75.99ms, Detection FPS: 13.2, total FPS: 11.3\n","Time: 75.71ms, Detection FPS: 13.2, total FPS: 11.3\n","Time: 75.63ms, Detection FPS: 13.2, total FPS: 11.4\n","Time: 75.47ms, Detection FPS: 13.3, total FPS: 11.4\n","Time: 75.22ms, Detection FPS: 13.3, total FPS: 11.4\n","Time: 75.06ms, Detection FPS: 13.3, total FPS: 11.4\n","Time: 75.13ms, Detection FPS: 13.3, total FPS: 11.4\n","Time: 75.38ms, Detection FPS: 13.3, total FPS: 11.4\n","Time: 74.96ms, Detection FPS: 13.3, total FPS: 11.5\n","Time: 74.59ms, Detection FPS: 13.4, total FPS: 11.5\n","Time: 74.46ms, Detection FPS: 13.4, total FPS: 11.5\n","Time: 74.54ms, Detection FPS: 13.4, total FPS: 11.5\n","Time: 74.37ms, Detection FPS: 13.4, total FPS: 11.5\n","Time: 74.36ms, Detection FPS: 13.4, total FPS: 11.6\n","Time: 74.48ms, Detection FPS: 13.4, total FPS: 11.5\n","Time: 74.45ms, Detection FPS: 13.4, total FPS: 11.5\n","Time: 74.89ms, Detection FPS: 13.4, total FPS: 11.5\n","Time: 74.95ms, Detection FPS: 13.3, total FPS: 11.4\n","Time: 74.64ms, Detection FPS: 13.4, total FPS: 11.5\n","Time: 74.87ms, Detection FPS: 13.4, total FPS: 11.4\n","Time: 75.19ms, Detection FPS: 13.3, total FPS: 11.4\n","Time: 75.40ms, Detection FPS: 13.3, total FPS: 11.4\n","Time: 75.44ms, Detection FPS: 13.3, total FPS: 11.3\n","Time: 75.57ms, Detection FPS: 13.2, total FPS: 11.3\n","Time: 75.68ms, Detection FPS: 13.2, total FPS: 11.3\n","Time: 76.45ms, Detection FPS: 13.1, total FPS: 11.2\n","Time: 76.35ms, Detection FPS: 13.1, total FPS: 11.2\n","Time: 76.24ms, Detection FPS: 13.1, total FPS: 11.2\n","Time: 76.40ms, Detection FPS: 13.1, total FPS: 11.2\n","Time: 76.50ms, Detection FPS: 13.1, total FPS: 11.2\n","Time: 76.51ms, Detection FPS: 13.1, total FPS: 11.2\n","Time: 76.33ms, Detection FPS: 13.1, total FPS: 11.2\n","Time: 76.48ms, Detection FPS: 13.1, total FPS: 11.2\n","Time: 76.74ms, Detection FPS: 13.0, total FPS: 11.1\n","Time: 76.94ms, Detection FPS: 13.0, total FPS: 11.1\n","Time: 77.26ms, Detection FPS: 12.9, total FPS: 11.1\n","Time: 76.87ms, Detection FPS: 13.0, total FPS: 11.1\n","Time: 77.08ms, Detection FPS: 13.0, total FPS: 11.1\n","Time: 77.11ms, Detection FPS: 13.0, total FPS: 11.1\n","Time: 76.78ms, Detection FPS: 13.0, total FPS: 11.2\n","Time: 76.46ms, Detection FPS: 13.1, total FPS: 11.2\n","Time: 76.22ms, Detection FPS: 13.1, total FPS: 11.2\n","Time: 76.15ms, Detection FPS: 13.1, total FPS: 11.2\n","Time: 76.28ms, Detection FPS: 13.1, total FPS: 11.2\n","Time: 76.13ms, Detection FPS: 13.1, total FPS: 11.2\n","Time: 75.65ms, Detection FPS: 13.2, total FPS: 11.3\n","Time: 75.51ms, Detection FPS: 13.2, total FPS: 11.3\n","Time: 75.34ms, Detection FPS: 13.3, total FPS: 11.3\n","Time: 75.33ms, Detection FPS: 13.3, total FPS: 11.3\n","Time: 75.15ms, Detection FPS: 13.3, total FPS: 11.4\n","Time: 75.12ms, Detection FPS: 13.3, total FPS: 11.4\n","Time: 75.26ms, Detection FPS: 13.3, total FPS: 11.3\n","Time: 75.62ms, Detection FPS: 13.2, total FPS: 11.3\n","Time: 75.35ms, Detection FPS: 13.3, total FPS: 11.3\n","Time: 75.09ms, Detection FPS: 13.3, total FPS: 11.3\n","Time: 74.97ms, Detection FPS: 13.3, total FPS: 11.3\n","Time: 75.07ms, Detection FPS: 13.3, total FPS: 11.3\n","Time: 75.14ms, Detection FPS: 13.3, total FPS: 11.3\n","Time: 75.12ms, Detection FPS: 13.3, total FPS: 11.3\n","Time: 75.28ms, Detection FPS: 13.3, total FPS: 11.3\n","Time: 75.38ms, Detection FPS: 13.3, total FPS: 11.2\n","Time: 75.83ms, Detection FPS: 13.2, total FPS: 11.2\n","Time: 76.00ms, Detection FPS: 13.2, total FPS: 11.1\n","Time: 76.26ms, Detection FPS: 13.1, total FPS: 11.1\n","Time: 76.57ms, Detection FPS: 13.1, total FPS: 11.1\n","Time: 76.73ms, Detection FPS: 13.0, total FPS: 11.0\n","Time: 76.91ms, Detection FPS: 13.0, total FPS: 11.0\n","Time: 77.06ms, Detection FPS: 13.0, total FPS: 11.0\n","Time: 77.39ms, Detection FPS: 12.9, total FPS: 10.9\n","Time: 77.54ms, Detection FPS: 12.9, total FPS: 10.9\n","Time: 78.04ms, Detection FPS: 12.8, total FPS: 10.9\n","Time: 78.02ms, Detection FPS: 12.8, total FPS: 10.9\n","Time: 77.52ms, Detection FPS: 12.9, total FPS: 10.9\n","Time: 77.51ms, Detection FPS: 12.9, total FPS: 10.9\n","Time: 77.49ms, Detection FPS: 12.9, total FPS: 11.0\n","Time: 77.57ms, Detection FPS: 12.9, total FPS: 11.0\n","Time: 77.50ms, Detection FPS: 12.9, total FPS: 11.0\n","Time: 77.29ms, Detection FPS: 12.9, total FPS: 11.0\n","Time: 77.06ms, Detection FPS: 13.0, total FPS: 11.0\n","Time: 77.19ms, Detection FPS: 13.0, total FPS: 11.0\n","Time: 77.04ms, Detection FPS: 13.0, total FPS: 11.0\n","Time: 76.77ms, Detection FPS: 13.0, total FPS: 11.1\n","Time: 76.56ms, Detection FPS: 13.1, total FPS: 11.1\n","Time: 76.08ms, Detection FPS: 13.1, total FPS: 11.2\n","Time: 75.92ms, Detection FPS: 13.2, total FPS: 11.2\n","Time: 75.59ms, Detection FPS: 13.2, total FPS: 11.2\n","Time: 75.42ms, Detection FPS: 13.3, total FPS: 11.3\n","Time: 75.44ms, Detection FPS: 13.3, total FPS: 11.3\n","Time: 75.56ms, Detection FPS: 13.2, total FPS: 11.3\n","Time: 75.57ms, Detection FPS: 13.2, total FPS: 11.3\n","Time: 75.24ms, Detection FPS: 13.3, total FPS: 11.3\n","Time: 75.09ms, Detection FPS: 13.3, total FPS: 11.3\n","Time: 75.25ms, Detection FPS: 13.3, total FPS: 11.3\n","Time: 75.31ms, Detection FPS: 13.3, total FPS: 11.3\n","Time: 75.47ms, Detection FPS: 13.2, total FPS: 11.3\n","Time: 75.26ms, Detection FPS: 13.3, total FPS: 11.3\n","Time: 75.28ms, Detection FPS: 13.3, total FPS: 11.3\n","Time: 75.49ms, Detection FPS: 13.2, total FPS: 11.2\n","Time: 75.55ms, Detection FPS: 13.2, total FPS: 11.2\n","Time: 75.50ms, Detection FPS: 13.2, total FPS: 11.2\n","Time: 75.57ms, Detection FPS: 13.2, total FPS: 11.2\n","Time: 75.68ms, Detection FPS: 13.2, total FPS: 11.2\n","Time: 75.89ms, Detection FPS: 13.2, total FPS: 11.1\n","Time: 75.98ms, Detection FPS: 13.2, total FPS: 11.1\n","Time: 76.04ms, Detection FPS: 13.2, total FPS: 11.1\n","Time: 76.15ms, Detection FPS: 13.1, total FPS: 11.1\n","Time: 76.59ms, Detection FPS: 13.1, total FPS: 11.1\n","Time: 76.39ms, Detection FPS: 13.1, total FPS: 11.1\n","Time: 75.78ms, Detection FPS: 13.2, total FPS: 11.2\n","Time: 76.07ms, Detection FPS: 13.1, total FPS: 11.1\n","Time: 75.91ms, Detection FPS: 13.2, total FPS: 11.2\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"I9QzvM6plx7N"},"source":[""],"execution_count":null,"outputs":[]}]}
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/.gitignore b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..a50f3a15ad47dc23e18a7df1b536b127bee32071
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/.gitignore	
@@ -0,0 +1,3 @@
+*.pyc
+model_data
+configs.py
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/Collect_training_data.py b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/Collect_training_data.py
new file mode 100644
index 0000000000000000000000000000000000000000..124cf0253c88f85f68948f860b097a92f333f7e7
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/Collect_training_data.py	
@@ -0,0 +1,122 @@
+#================================================================
+#
+#   File name   : Collect_training_data.py
+#   Author      : PyLessons
+#   Created date: 2020-09-27
+#   Website     : https://pylessons.com/
+#   GitHub      : https://github.com/pythonlessons/TensorFlow-2.x-YOLOv3
+#   Description : YOLO detection to XML example script
+#
+#================================================================
+import os
+import subprocess
+import time
+from datetime import datetime
+import cv2
+import mss
+import numpy as np
+import tensorflow as tf
+from yolov3.utils import *
+from yolov3.configs import *
+from yolov3.yolov4 import read_class_names
+from tools.Detection_to_XML import CreateXMLfile
+import random
+
+def draw_enemy(image, bboxes, CLASSES=YOLO_COCO_CLASSES, show_label=True, show_confidence = True, Text_colors=(255,255,0), rectangle_colors='', tracking=False):   
+    NUM_CLASS = read_class_names(CLASSES)
+    num_classes = len(NUM_CLASS)
+    image_h, image_w, _ = image.shape
+    hsv_tuples = [(1.0 * x / num_classes, 1., 1.) for x in range(num_classes)]
+    colors = list(map(lambda x: colorsys.hsv_to_rgb(*x), hsv_tuples))
+    colors = list(map(lambda x: (int(x[0] * 255), int(x[1] * 255), int(x[2] * 255)), colors))
+
+    random.seed(0)
+    random.shuffle(colors)
+    random.seed(None)
+
+    detection_list = []
+
+    for i, bbox in enumerate(bboxes):
+        coor = np.array(bbox[:4], dtype=np.int32)
+        score = bbox[4]
+        class_ind = int(bbox[5])
+        bbox_color = rectangle_colors if rectangle_colors != '' else colors[class_ind]
+        bbox_thick = int(0.6 * (image_h + image_w) / 1000)
+        if bbox_thick < 1: bbox_thick = 1
+        fontScale = 0.75 * bbox_thick
+        (x1, y1), (x2, y2) = (coor[0], coor[1]), (coor[2], coor[3])
+
+        # put object rectangle
+        cv2.rectangle(image, (x1, y1), (x2, y2), bbox_color, bbox_thick*2)
+
+        x, y = int(x1+(x2-x1)/2), int(y1+(y2-y1)/2)
+
+        if show_label:
+            # get text label
+            score_str = " {:.2f}".format(score) if show_confidence else ""
+
+            if tracking: score_str = " "+str(score)
+
+            label = "{}".format(NUM_CLASS[class_ind]) + score_str
+
+            # get text size
+            (text_width, text_height), baseline = cv2.getTextSize(label, cv2.FONT_HERSHEY_COMPLEX_SMALL,
+                                                                  fontScale, thickness=bbox_thick)
+            # put filled text rectangle
+            cv2.rectangle(image, (x1, y1), (x1 + text_width, y1 - text_height - baseline), bbox_color, thickness=cv2.FILLED)
+
+            # put text above rectangle
+            cv2.putText(image, label, (x1, y1-4), cv2.FONT_HERSHEY_COMPLEX_SMALL, fontScale, Text_colors, bbox_thick, lineType=cv2.LINE_AA)
+
+    return image
+
+def detect_enemy(Yolo, original_image, input_size=416, CLASSES=YOLO_COCO_CLASSES, score_threshold=0.3, iou_threshold=0.45, rectangle_colors=''):
+    image_data = image_preprocess(original_image, [input_size, input_size])
+    image_data = image_data[np.newaxis, ...].astype(np.float32)
+
+    if YOLO_FRAMEWORK == "tf":
+        pred_bbox = Yolo.predict(image_data)
+
+    elif YOLO_FRAMEWORK == "trt":
+        batched_input = tf.constant(image_data)
+        result = Yolo(batched_input)
+        pred_bbox = []
+        for key, value in result.items():
+            value = value.numpy()
+            pred_bbox.append(value)
+        
+    pred_bbox = [tf.reshape(x, (-1, tf.shape(x)[-1])) for x in pred_bbox]
+    pred_bbox = tf.concat(pred_bbox, axis=0)
+    
+    bboxes = postprocess_boxes(pred_bbox, original_image, input_size, score_threshold)
+    bboxes = nms(bboxes, iou_threshold, method='nms')
+
+    image = draw_enemy(original_image, bboxes, CLASSES=CLASSES, rectangle_colors=rectangle_colors)
+        
+    return image, bboxes
+
+offset = 30
+times = []
+sct = mss.mss()
+yolo = Load_Yolo_model()
+while True:
+    t1 = time.time()
+    img = np.array(sct.grab({"top": 87-offset, "left": 1920, "width": 1280, "height": 720, "mon": -1}))
+    img = cv2.cvtColor(img, cv2.COLOR_RGBA2RGB)
+    image, bboxes = detect_enemy(yolo, np.copy(img), input_size=YOLO_INPUT_SIZE, CLASSES=TRAIN_CLASSES, rectangle_colors=(255,0,0))
+    if len(bboxes) > 0:
+        CreateXMLfile("XML_Detections", str(int(time.time())), img, bboxes, read_class_names(TRAIN_CLASSES))
+        print("got it")
+        time.sleep(2)
+    
+    t2 = time.time()
+    times.append(t2-t1)
+    times = times[-20:]
+    ms = sum(times)/len(times)*1000
+    fps = 1000 / ms
+    print("FPS", fps)
+    
+    #cv2.imshow("Detection image", img)
+    #if cv2.waitKey(25) & 0xFF == ord("q"):
+        #cv2.destroyAllWindows()
+        #break
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/IMAGES/cone.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/IMAGES/cone.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..8fd4668bb1622e3e5daf6cd01f5ee3dbce74f6b9
Binary files /dev/null and b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/IMAGES/cone.jpg differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/IMAGES/cone_detect.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/IMAGES/cone_detect.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..eb9b0793d8b024737cb172cb3f46ce16e70bce3b
Binary files /dev/null and b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/IMAGES/cone_detect.jpg differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/LICENSE b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..2733f4f504e98a76ab4b12bbb88535ad89e01a21
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/LICENSE	
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2022 pythonlessons
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/README.md b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..9e2e6b4d8764ea3123a5f916d8a0d6c8d5e00bb6
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+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/README.md	
@@ -0,0 +1,170 @@
+# TensorFlow-2.x-YOLOv3 and YOLOv4 tutorials
+
+YOLOv3 and YOLOv4 implementation in TensorFlow 2.x, with support for training, transfer training, object tracking mAP and so on...
+Code was tested with following specs:
+- i7-7700k CPU and Nvidia 1080TI GPU
+- OS Ubuntu 18.04
+- CUDA 10.1
+- cuDNN v7.6.5
+- TensorRT-6.0.1.5
+- Tensorflow-GPU 2.3.1
+- Code was tested on Ubuntu and Windows 10 (TensorRT not supported officially)
+
+## Installation
+First, clone or download this GitHub repository.
+Install requirements and download pretrained weights:
+```
+pip install -r ./requirements.txt
+
+# yolov3
+wget -P model_data https://pjreddie.com/media/files/yolov3.weights
+
+# yolov3-tiny
+wget -P model_data https://pjreddie.com/media/files/yolov3-tiny.weights
+
+# yolov4
+wget -P model_data https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v3_optimal/yolov4.weights
+
+# yolov4-tiny
+wget -P model_data https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-tiny.weights
+```
+
+## Quick start
+Start with using pretrained weights to test predictions on both image and video:
+```
+python detection_demo.py
+```
+
+<p align="center">
+    <img width="100%" src="IMAGES/city_pred.jpg" style="max-width:100%;"></a>
+</p>
+
+## Quick training for custom mnist dataset
+mnist folder contains mnist images, create training data:
+```
+python mnist/make_data.py
+```
+`./yolov3/configs.py` file is already configured for mnist training.
+
+Now, you can train it and then evaluate your model
+```
+python train.py
+tensorboard --logdir=log
+```
+Track training progress in Tensorboard and go to http://localhost:6006/:
+<p align="center">
+    <img width="100%" src="IMAGES/tensorboard.png" style="max-width:100%;"></a>
+</p>
+
+Test detection with `detect_mnist.py` script:
+```
+python detect_mnist.py
+```
+Results:
+<p align="center">
+    <img width="40%" src="IMAGES/mnist_test.jpg" style="max-width:40%;"></a>
+</p>
+
+## Custom YOLOv3 & YOLOv4 object detection training
+Custom training required to prepare dataset first, how to prepare dataset and train custom model you can read in following link:<br>
+https://pylessons.com/YOLOv3-TF2-custrom-train/<br>
+More about YOLOv4 training you can read [on this link](https://pylessons.com/YOLOv4-TF2-training/). I didn’t have time to implement all YOLOv4 Bag-Of-Freebies to improve the training process… Maybe later I’ll find time to do that, but now I leave it as it is. I recommended to use [Alex's Darknet](https://github.com/AlexeyAB/darknet) to train your custom model, if you need maximum performance, otherwise, you can use my implementation.
+
+## Google Colab Custom Yolo v3 training
+To learn more about Google Colab Free gpu training, visit my [text version tutorial](https://pylessons.com/YOLOv3-TF2-GoogleColab/)
+
+## Yolo v3 Tiny train and detection
+To get detailed instructions how to use Yolov3-Tiny, follow my text version tutorial [YOLOv3-Tiny support](https://pylessons.com/YOLOv3-TF2-Tiny/). Short instructions:
+- Get YOLOv3-Tiny weights: ```wget -P model_data https://pjreddie.com/media/files/yolov3-tiny.weights```
+- From `yolov3/configs.py` change `TRAIN_YOLO_TINY` from `False` to `True`
+- Run `detection_demo.py` script.
+
+## Yolo v3 Object tracking
+To learn more about Object tracking with Deep SORT, visit [Following link](https://pylessons.com/YOLOv3-TF2-DeepSort/).
+Quick test:
+- Clone this repository;
+- Make sure object detection works for you;
+- Run object_tracking.py script
+<p align="center">
+    <img src="IMAGES/tracking_results.gif"></a>
+</p>
+
+## YOLOv3 vs YOLOv4 comparison on 1080TI:
+
+YOLO FPS on COCO 2017 Dataset:
+| Detection    | 320x320 | 416x416 | 512x512 |
+|--------------|---------|---------|---------|
+| YoloV3 FPS   | 24.38   | 20.94   | 18.57   |
+| YoloV4 FPS   | 22.15   | 18.69   | 16.50   |
+
+TensorRT FPS on COCO 2017 Dataset:
+| Detection       | 320x320 | 416x416 | 512x512 | 608x608 |
+|-----------------|---------|---------|---------|---------|
+| YoloV4 FP32 FPS | 31.23   | 27.30   | 22.63   | 18.17   |
+| YoloV4 FP16 FPS | 30.33   | 25.44   | 21.94   | 17.99   |
+| YoloV4 INT8 FPS | 85.18   | 62.02   | 47.50   | 37.32   |
+| YoloV3 INT8 FPS | 84.65   | 52.72   | 38.22   | 28.75   |
+
+mAP on COCO 2017 Dataset:
+| Detection        | 320x320 | 416x416 | 512x512 |
+|------------------|---------|---------|---------|
+| YoloV3 mAP50     | 49.85   | 55.31   | 57.48   |         
+| YoloV4 mAP50     | 48.58   | 56.92   | 61.71   |         
+
+TensorRT mAP on COCO 2017 Dataset:
+| Detection         | 320x320 | 416x416 | 512x512 | 608x608 |
+|-------------------|---------|---------|---------|---------|
+| YoloV4 FP32 mAP50 | 48.58   | 56.92   | 61.71   | 63.92   |
+| YoloV4 FP16 mAP50 | 48.57   | 56.92   | 61.69   | 63.92   |
+| YoloV4 INT8 mAP50 | 40.61   | 48.36   | 52.84   | 54.53   |
+| YoloV3 INT8 mAP50 | 44.19   | 48.64   | 50.10   | 50.69   |
+
+## Converting YOLO to TensorRT
+I will give two examples, both will be for YOLOv4 model,quantize_mode=INT8 and model input size will be 608. Detailed tutorial is on this [link](https://pylessons.com/YOLOv4-TF2-TensorRT/).
+### Default weights from COCO dataset:
+- Download weights from links above;
+- In `configs.py` script choose your `YOLO_TYPE`;
+- In `configs.py` script set `YOLO_INPUT_SIZE = 608`;
+- In `configs.py` script set `YOLO_FRAMEWORK = "trt"`;
+- From main directory in terminal type `python tools/Convert_to_pb.py`;
+- From main directory in terminal type `python tools/Convert_to_TRT.py`;
+- In `configs.py` script set `YOLO_CUSTOM_WEIGHTS = f'checkpoints/{YOLO_TYPE}-trt-{YOLO_TRT_QUANTIZE_MODE}–{YOLO_INPUT_SIZE}'`;
+- Now you can run `detection_demo.py`, best to test with `detect_video` function.
+
+### Custom trained YOLO weights:
+- Download weights from links above;
+- In `configs.py` script choose your `YOLO_TYPE`;
+- In `configs.py` script set `YOLO_INPUT_SIZE = 608`;
+- Train custom YOLO model with instructions above;
+- In `configs.py` script set `YOLO_CUSTOM_WEIGHTS = f"{YOLO_TYPE}_custom"`;
+- In `configs.py` script make sure that  `TRAIN_CLASSES` is with your custom classes text file;
+- From main directory in terminal type `python tools/Convert_to_pb.py`;
+- From main directory in terminal type `python tools/Convert_to_TRT.py`;
+- In `configs.py` script set `YOLO_FRAMEWORK = "trt"`;
+- In `configs.py` script set `YOLO_CUSTOM_WEIGHTS = f'checkpoints/{YOLO_TYPE}-trt-{YOLO_TRT_QUANTIZE_MODE}–{YOLO_INPUT_SIZE}'`;
+- Now you can run `detection_custom.py`, to test custom trained and converted TensorRT model.
+
+What is done:
+--------------------
+- [x] Detection with original weights [Tutorial link](https://pylessons.com/YOLOv3-TF2-introduction/)
+- [x] Mnist detection training [Tutorial link](https://pylessons.com/YOLOv3-TF2-mnist/)
+- [x] Custom detection training [Tutorial link1](https://pylessons.com/YOLOv3-TF2-custrom-train/), [link2](https://pylessons.com/YOLOv3-TF2-custrom-images/)
+- [x] Google Colab training [Tutorial link](https://pylessons.com/YOLOv3-TF2-GoogleColab/)
+- [x] YOLOv3-Tiny support [Tutorial link](https://pylessons.com/YOLOv3-TF2-Tiny/)
+- [X] Object tracking [Tutorial link](https://pylessons.com/YOLOv3-TF2-DeepSort/)
+- [X] Mean Average Precision (mAP) [Tutorial link](https://pylessons.com/YOLOv3-TF2-mAP/)
+- [X] Yolo v3 on Raspberry Pi [Tutorial link](https://pylessons.com/YOLOv3-TF2-RaspberryPi/)
+- [X] YOLOv4 and YOLOv4-tiny detection [Tutorial link](https://pylessons.com/YOLOv4-TF2-introduction/)
+- [X] YOLOv4 and YOLOv4-tiny detection training (Not fully) [Tutorial link](https://pylessons.com/YOLOv4-TF2-training/)
+- [X] Convert to TensorRT model [Tutorial link](https://pylessons.com/YOLOv4-TF2-TensorRT/)
+- [X] Add multiprocessing after detection (drawing bbox) [Tutorial link](https://pylessons.com/YOLOv4-TF2-multiprocessing/)
+- [X] Generate YOLO Object Detection training data from its own results [Tutorial link](https://pylessons.com/YOLOv4-TF2-CreateXML/)
+- [X] Counter-strike Global Offensive realtime YOLOv4 Object Detection aimbot [Tutorial link](https://pylessons.com/YOLOv4-TF2-CSGO-aimbot/)
+
+To be continued... (not anytime soon)
+--------------------
+- [ ] Converting to TensorFlow Lite
+- [ ] YOLO on Android (Leaving it for future, will need to convert everythin to java... not ready for this)
+- [ ] Generating anchors
+- [ ] YOLACT: Real-time Instance Segmentation
+- [ ] Model pruning (Pruning is a technique in deep learning that aids in the development of smaller and more efficient neural networks. It's a model optimization technique that involves eliminating unnecessary values in the weight tensor.)
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/YOLOv3_colab_training.ipynb b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/YOLOv3_colab_training.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..c61593049a347b3ec4b1ed55447f57b7ba83aebc
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/YOLOv3_colab_training.ipynb	
@@ -0,0 +1 @@
+{"nbformat":4,"nbformat_minor":0,"metadata":{"accelerator":"GPU","colab":{"name":"YOLOv3_colab_training.ipynb","provenance":[],"collapsed_sections":[]},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.6.9"}},"cells":[{"cell_type":"markdown","metadata":{"id":"twluDsqOaqRW"},"source":["\n","========================================================<br>\n","<br>\n","   File name   : YOLOv3_colab_training.ipynb<br>\n","   Author      : PyLessons<br>\n","   Created date: 2020-09-30<br>\n","   Website     : https://pylessons.com/YOLOv3-TF2-GoogleColab<br>\n","   GitHub      : https://github.com/pythonlessons/TensorFlow-2.x-YOLOv3<br>\n","   Description : Train custom model on Google colab tutorial<br>\n","<br>\n","================================================================\n","\n","\n","**Open this notebook from google drive**<br>\n","**Go to \"Edit\" -> \"Notebook settings\" and enable GPU.**\n"]},{"cell_type":"code","metadata":{"id":"srBiJiFEaKl1","executionInfo":{"status":"ok","timestamp":1601446581065,"user_tz":-180,"elapsed":1009,"user":{"displayName":"Python Lessons","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgMQmMhFapKcavl337-vY17yrbowBHBlZQ5qYQv=s64","userId":"12382394757900236362"}},"outputId":"b3a94fa4-c78c-4db9-d400-f14bf19732e0","colab":{"base_uri":"https://localhost:8080/","height":357}},"source":["# Check if NVIDIA GPU is enabled\n","!nvidia-smi"],"execution_count":1,"outputs":[{"output_type":"stream","text":["Wed Sep 30 06:16:20 2020       \n","+-----------------------------------------------------------------------------+\n","| NVIDIA-SMI 455.23.05    Driver Version: 418.67       CUDA Version: 10.1     |\n","|-------------------------------+----------------------+----------------------+\n","| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n","| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n","|                               |                      |               MIG M. |\n","|===============================+======================+======================|\n","|   0  Tesla P100-PCIE...  Off  | 00000000:00:04.0 Off |                    0 |\n","| N/A   41C    P0    28W / 250W |      0MiB / 16280MiB |      0%      Default |\n","|                               |                      |                 ERR! |\n","+-------------------------------+----------------------+----------------------+\n","                                                                               \n","+-----------------------------------------------------------------------------+\n","| Processes:                                                                  |\n","|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n","|        ID   ID                                                   Usage      |\n","|=============================================================================|\n","|  No running processes found                                                 |\n","+-----------------------------------------------------------------------------+\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"sj3eAw1OXOnB"},"source":["**Connect and authorize google drive with google colab:**"]},{"cell_type":"code","metadata":{"id":"PjjcQSpya_FR","executionInfo":{"status":"ok","timestamp":1601446598010,"user_tz":-180,"elapsed":17936,"user":{"displayName":"Python Lessons","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgMQmMhFapKcavl337-vY17yrbowBHBlZQ5qYQv=s64","userId":"12382394757900236362"}},"outputId":"c6e2509b-27ea-45c0-e923-a8fc89e52f3d","colab":{"base_uri":"https://localhost:8080/","height":51}},"source":["from google.colab import drive\n","drive.mount('/content/gdrive')\n","!ls"],"execution_count":2,"outputs":[{"output_type":"stream","text":["Mounted at /content/gdrive\n","gdrive\tsample_data\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"h5GywMhIKCd0"},"source":["**Open our project \"TensorFlow-2.x-YOLOv3\" direcotry in google drive:**"]},{"cell_type":"code","metadata":{"id":"iYM4wmy-cFlK","executionInfo":{"status":"ok","timestamp":1601446598379,"user_tz":-180,"elapsed":3922,"user":{"displayName":"Python Lessons","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgMQmMhFapKcavl337-vY17yrbowBHBlZQ5qYQv=s64","userId":"12382394757900236362"}},"outputId":"d7879aa3-ca3c-4e1f-f7c5-6685f56f07cf","colab":{"base_uri":"https://localhost:8080/","height":170}},"source":["%cd gdrive/My\\ Drive/TensorFlow-2.x-YOLOv3/\n","!ls"],"execution_count":3,"outputs":[{"output_type":"stream","text":["/content/gdrive/My Drive/TensorFlow-2.x-YOLOv3\n","CAPTCHA_solver_v3.h5  IMAGES\t\t __pycache__\n","checkpoints\t      log\t\t requirements.txt\n","custom_dataset\t      mAP\t\t save_program_screen.py\n","deep_sort\t      mnist\t\t tools\n","detection_custom.py   model_data\t train.py\n","detection_demo.py     mp_test.py\t yolov3\n","detect_mnist.py       multiprc.py\t YOLOv3_colab_training.ipynb\n","evaluate_mAP.py       object_tracker.py\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"24u5FY2ZKbrc"},"source":["**Install all required libraries for our project:**"]},{"cell_type":"code","metadata":{"id":"adhpOaKT9lWC"},"source":["!pip install -r ./requirements.txt"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"1DMiQtY7Koy7"},"source":["**Download yolov3.weights if you don't have it:**"]},{"cell_type":"code","metadata":{"id":"UX-LG3R6An5U"},"source":["!wget -P model_data https://pjreddie.com/media/files/yolov3.weights"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"DDmBbAUKLUkB"},"source":["**Test if TensorFlow works with gpu for you, in output should see similar results:**\n","```\n","2.3.0\n","'/device:GPU:0'\n","```"]},{"cell_type":"code","metadata":{"id":"M3cWo7hhc-qO","executionInfo":{"status":"ok","timestamp":1601446634334,"user_tz":-180,"elapsed":7965,"user":{"displayName":"Python Lessons","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgMQmMhFapKcavl337-vY17yrbowBHBlZQ5qYQv=s64","userId":"12382394757900236362"}},"outputId":"1a7edf1a-a3a9-450e-ace2-34bf1ad2c6a8","colab":{"base_uri":"https://localhost:8080/","height":52}},"source":["import tensorflow as tf\n","print(tf.__version__)\n","tf.test.gpu_device_name()"],"execution_count":5,"outputs":[{"output_type":"stream","text":["2.3.0\n"],"name":"stdout"},{"output_type":"execute_result","data":{"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"},"text/plain":["'/device:GPU:0'"]},"metadata":{"tags":[]},"execution_count":5}]},{"cell_type":"markdown","metadata":{"id":"sX0TGlJhMGd_"},"source":["**Test by loading trained model:**"]},{"cell_type":"code","metadata":{"id":"NUKLydfYCo4r","executionInfo":{"status":"ok","timestamp":1601446652047,"user_tz":-180,"elapsed":16498,"user":{"displayName":"Python Lessons","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgMQmMhFapKcavl337-vY17yrbowBHBlZQ5qYQv=s64","userId":"12382394757900236362"}}},"source":["import cv2\n","import numpy as np\n","import matplotlib\n","import matplotlib.pyplot as plt\n","%matplotlib inline  \n","import tensorflow as tf\n","from yolov3.yolov4 import Create_Yolo\n","from yolov3.utils import load_yolo_weights, detect_image\n","from yolov3.configs import *\n","\n","if YOLO_TYPE == \"yolov4\":\n","    Darknet_weights = YOLO_V4_TINY_WEIGHTS if TRAIN_YOLO_TINY else YOLO_V4_WEIGHTS\n","if YOLO_TYPE == \"yolov3\":\n","    Darknet_weights = YOLO_V3_TINY_WEIGHTS if TRAIN_YOLO_TINY else YOLO_V3_WEIGHTS\n","\n","yolo = Create_Yolo(input_size=YOLO_INPUT_SIZE)\n","load_yolo_weights(yolo, Darknet_weights) # use Darknet weights"],"execution_count":6,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"qdpGgKUUMJOe"},"source":["**Test by testing detection on original model:**"]},{"cell_type":"code","metadata":{"id":"PcNcPmvLC5fl","executionInfo":{"status":"ok","timestamp":1601446675762,"user_tz":-180,"elapsed":19238,"user":{"displayName":"Python Lessons","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgMQmMhFapKcavl337-vY17yrbowBHBlZQ5qYQv=s64","userId":"12382394757900236362"}},"outputId":"52d4b8fa-aa52-47a4-a835-5342342e073c","colab":{"base_uri":"https://localhost:8080/","height":884,"output_embedded_package_id":"1yJ7Wt-vDQ42A4wLCjMsACdBP8uCGWsSG"}},"source":["image_path   = \"./IMAGES/street.jpg\"\n","\n","image = detect_image(yolo, image_path, '', input_size=YOLO_INPUT_SIZE, show=False, rectangle_colors=(255,0,0))\n","image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n","\n","plt.figure(figsize=(30,15))\n","plt.imshow(image)"],"execution_count":7,"outputs":[{"output_type":"display_data","data":{"text/plain":"Output hidden; open in https://colab.research.google.com to view."},"metadata":{}}]},{"cell_type":"markdown","metadata":{"id":"f81_fxA5Naqm"},"source":["**Run `XML_to_YOLOv3.py` script to convert XML files to YOLOv3 annotations files:**"]},{"cell_type":"code","metadata":{"id":"pXlFGBAp7Ibg","executionInfo":{"status":"ok","timestamp":1596814215969,"user_tz":-180,"elapsed":428158,"user":{"displayName":"Python Lessons","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgMQmMhFapKcavl337-vY17yrbowBHBlZQ5qYQv=s64","userId":"12382394757900236362"}},"outputId":"340f3942-c2b3-4e2a-bf5f-267e12d696fb","colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["!python tools/XML_to_YOLOv3.py"],"execution_count":null,"outputs":[{"output_type":"stream","text":["/content/gdrive/My 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309,542,569,611,0\n","/content/gdrive/My Drive/TensorFlow-2.x-YOLOv3/custom_dataset/test/dayride_type1_002.mp4#t=75.jpg 97,608,310,679,0\n","/content/gdrive/My Drive/TensorFlow-2.x-YOLOv3/custom_dataset/test/dayride_type1_001.mp4#t=793.jpg 538,337,619,357,0\n","/content/gdrive/My Drive/TensorFlow-2.x-YOLOv3/custom_dataset/test/dayride_type1_001.mp4#t=491.jpg 144,351,212,369,0 519,389,584,405,0\n","/content/gdrive/My Drive/TensorFlow-2.x-YOLOv3/custom_dataset/test/nightride_type3_001.mp4#t=163.jpg 525,405,679,449,0\n","/content/gdrive/My Drive/TensorFlow-2.x-YOLOv3/custom_dataset/test/dayride_type1_003.mp4#t=44.jpg 822,344,909,371,0\n","/content/gdrive/My Drive/TensorFlow-2.x-YOLOv3/custom_dataset/test/dayride_type1_001.mp4#t=147.jpg 621,333,667,348,0\n","/content/gdrive/My Drive/TensorFlow-2.x-YOLOv3/custom_dataset/test/nightride_type3_001.mp4#t=358.jpg 0,410,144,452,0\n","/content/gdrive/My Drive/TensorFlow-2.x-YOLOv3/custom_dataset/test/dayride_type1_001.mp4#t=595.jpg 626,432,772,470,0\n","/content/gdrive/My Drive/TensorFlow-2.x-YOLOv3/custom_dataset/test/dayride_type1_001.mp4#t=1456.jpg 50,452,164,483,0\n","/content/gdrive/My Drive/TensorFlow-2.x-YOLOv3/custom_dataset/test/dayride_type1_001.mp4#t=771.jpg 555,408,754,454,0\n","/content/gdrive/My Drive/TensorFlow-2.x-YOLOv3/custom_dataset/test/dayride_type1_001.mp4#t=1085.jpg 583,346,662,367,0\n","Dataset_names: ['license-plate']\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"5CYGwaPfV3H6"},"source":["**Start training custom model:**"]},{"cell_type":"code","metadata":{"id":"rUxAdSEQEdpG"},"source":["from train import *\n","tf.keras.backend.clear_session()\n","main()"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"7YNbZxosPRNw"},"source":["**Create Yolo v3 custom model and load custom trained weights**"]},{"cell_type":"code","metadata":{"id":"W5CuaoSI3KRm","executionInfo":{"status":"ok","timestamp":1601446697304,"user_tz":-180,"elapsed":13637,"user":{"displayName":"Python Lessons","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgMQmMhFapKcavl337-vY17yrbowBHBlZQ5qYQv=s64","userId":"12382394757900236362"}},"outputId":"684fc60e-fd68-4378-9e3d-7f2ab599e439","colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["yolo = Create_Yolo(input_size=YOLO_INPUT_SIZE, CLASSES=TRAIN_CLASSES)\n","yolo.load_weights(\"./checkpoints/yolov3_custom\") # use keras weights"],"execution_count":8,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<tensorflow.python.training.tracking.util.CheckpointLoadStatus at 0x7f9d9124e128>"]},"metadata":{"tags":[]},"execution_count":8}]},{"cell_type":"markdown","metadata":{"id":"dwqeCYh_PTuw"},"source":["**Test the detection with `IMAGES/plate_2.jpg` image**"]},{"cell_type":"code","metadata":{"id":"bx94uGmLPJz5","executionInfo":{"status":"ok","timestamp":1601446712563,"user_tz":-180,"elapsed":9187,"user":{"displayName":"Python Lessons","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgMQmMhFapKcavl337-vY17yrbowBHBlZQ5qYQv=s64","userId":"12382394757900236362"}},"outputId":"64ed8c06-805e-40cd-8df2-792b043d5a74","colab":{"base_uri":"https://localhost:8080/","height":883}},"source":["image_path   = \"./IMAGES/plate_1.jpg\"\n","image = detect_image(yolo, image_path, \"\", input_size=YOLO_INPUT_SIZE, show=False, CLASSES=TRAIN_CLASSES, rectangle_colors=(255,0,0))\n","image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n","\n","plt.figure(figsize=(30,15))\n","plt.imshow(image)"],"execution_count":9,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.image.AxesImage at 0x7f9d90e9e6d8>"]},"metadata":{"tags":[]},"execution_count":9},{"output_type":"display_data","data":{"image/png":"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size 2160x1080 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"7dKA8DYOj8d-"},"source":["# **You just trained your first Yolo v3 custom object detector on google colab, GOOD JOB!!**"]},{"cell_type":"code","metadata":{"id":"LQHYhLbEkZxh"},"source":[""],"execution_count":null,"outputs":[]}]}
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/checkpoints/checkpoint b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/checkpoints/checkpoint
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@@ -0,0 +1,2 @@
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+all_model_checkpoint_paths: "yolov3_custom"
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/checkpoints/yolov3_custom.data-00000-of-00002 b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/checkpoints/yolov3_custom.data-00000-of-00002
new file mode 100644
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--- /dev/null
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/1.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/1.jpg
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+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>1.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\1.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>414</width>
+		<height>414</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>112</xmin>
+			<ymin>35</ymin>
+			<xmax>309</xmax>
+			<ymax>327</ymax>
+		</bndbox>
+	</object>
+</annotation>
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/10.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>10.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\10.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>508</width>
+		<height>337</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>248</xmin>
+			<ymin>39</ymin>
+			<xmax>436</xmax>
+			<ymax>328</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/100.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/100.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/100.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/100.xml
new file mode 100644
index 0000000000000000000000000000000000000000..a4f1e281d26fbb19a991c5b4a2aec1439a86652f
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/100.xml	
@@ -0,0 +1,62 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>100.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\100.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>509</width>
+		<height>339</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>40</xmin>
+			<ymin>66</ymin>
+			<xmax>229</xmax>
+			<ymax>319</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>197</xmin>
+			<ymin>73</ymin>
+			<xmax>312</xmax>
+			<ymax>248</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>323</xmin>
+			<ymin>76</ymin>
+			<xmax>387</xmax>
+			<ymax>171</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>174</xmin>
+			<ymin>80</ymin>
+			<xmax>243</xmax>
+			<ymax>201</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/101.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/101.jpg
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Binary files /dev/null and b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/101.jpg differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/101.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/101.xml
new file mode 100644
index 0000000000000000000000000000000000000000..958c11046fa5c40a05c221f6776edf732e33ef6a
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/101.xml	
@@ -0,0 +1,62 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>101.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\101.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>336</width>
+		<height>509</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>242</xmin>
+			<ymin>383</ymin>
+			<xmax>267</xmax>
+			<ymax>436</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>171</xmin>
+			<ymin>377</ymin>
+			<xmax>213</xmax>
+			<ymax>437</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>107</xmin>
+			<ymin>381</ymin>
+			<xmax>143</xmax>
+			<ymax>434</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>32</xmin>
+			<ymin>378</ymin>
+			<xmax>73</xmax>
+			<ymax>436</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/102.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/102.jpg
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Binary files /dev/null and b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/102.jpg differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/102.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/102.xml
new file mode 100644
index 0000000000000000000000000000000000000000..0b575db6936b0321e789d26d19ac36ba7a6ff930
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/102.xml	
@@ -0,0 +1,74 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>102.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\102.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>359</width>
+		<height>479</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>190</xmin>
+			<ymin>275</ymin>
+			<xmax>295</xmax>
+			<ymax>430</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>212</xmin>
+			<ymin>260</ymin>
+			<xmax>297</xmax>
+			<ymax>384</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>234</xmin>
+			<ymin>234</ymin>
+			<xmax>305</xmax>
+			<ymax>345</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>264</xmin>
+			<ymin>226</ymin>
+			<xmax>324</xmax>
+			<ymax>319</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>283</xmin>
+			<ymin>214</ymin>
+			<xmax>326</xmax>
+			<ymax>289</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/103.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/103.jpg
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Binary files /dev/null and b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/103.jpg differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/103.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/103.xml
new file mode 100644
index 0000000000000000000000000000000000000000..c717ab0e7d044d5ff6b1ef6360c90f5f50017f4a
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/103.xml	
@@ -0,0 +1,62 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>103.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\103.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>525</width>
+		<height>329</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>124</xmin>
+			<ymin>127</ymin>
+			<xmax>228</xmax>
+			<ymax>296</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>6</xmin>
+			<ymin>69</ymin>
+			<xmax>93</xmax>
+			<ymax>194</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>255</xmin>
+			<ymin>36</ymin>
+			<xmax>321</xmax>
+			<ymax>145</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>423</xmin>
+			<ymin>81</ymin>
+			<xmax>519</xmax>
+			<ymax>223</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/104.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/104.jpg
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Binary files /dev/null and b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/104.jpg differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/104.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/104.xml
new file mode 100644
index 0000000000000000000000000000000000000000..1c2a4ab2cd64de1abb20dbe9000820543b202a39
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/104.xml	
@@ -0,0 +1,62 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>104.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\104.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>449</width>
+		<height>382</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>127</xmin>
+			<ymin>71</ymin>
+			<xmax>261</xmax>
+			<ymax>300</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>70</xmin>
+			<ymin>96</ymin>
+			<xmax>168</xmax>
+			<ymax>288</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>281</xmin>
+			<ymin>31</ymin>
+			<xmax>449</xmax>
+			<ymax>338</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>255</xmin>
+			<ymin>87</ymin>
+			<xmax>350</xmax>
+			<ymax>289</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/105.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/105.jpg
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Binary files /dev/null and b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/105.jpg differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/105.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/105.xml
new file mode 100644
index 0000000000000000000000000000000000000000..b19e2dba6aef0ea08aa7a2701e7dd202bf9a830b
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/105.xml	
@@ -0,0 +1,86 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>105.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\105.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>445</width>
+		<height>594</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>1</xmin>
+			<ymin>57</ymin>
+			<xmax>182</xmax>
+			<ymax>569</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>44</xmin>
+			<ymin>53</ymin>
+			<xmax>237</xmax>
+			<ymax>479</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>133</xmin>
+			<ymin>66</ymin>
+			<xmax>296</xmax>
+			<ymax>409</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>204</xmin>
+			<ymin>62</ymin>
+			<xmax>333</xmax>
+			<ymax>364</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>277</xmin>
+			<ymin>43</ymin>
+			<xmax>397</xmax>
+			<ymax>315</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>330</xmin>
+			<ymin>33</ymin>
+			<xmax>429</xmax>
+			<ymax>275</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/106.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/106.jpg
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Binary files /dev/null and b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/106.jpg differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/106.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/106.xml
new file mode 100644
index 0000000000000000000000000000000000000000..e0d0731395371c618affdf81c108809c4e1ad1fb
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/106.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>106.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\106.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>170</width>
+		<height>108</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>5</xmin>
+			<ymin>23</ymin>
+			<xmax>102</xmax>
+			<ymax>86</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/107.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/107.jpg
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Binary files /dev/null and b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/107.jpg differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/107.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/107.xml
new file mode 100644
index 0000000000000000000000000000000000000000..7df09c28143339caef9663afa46044eacf8aec12
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/107.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>107.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\107.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>493</width>
+		<height>594</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>189</xmin>
+			<ymin>224</ymin>
+			<xmax>316</xmax>
+			<ymax>441</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/108.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/108.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/108.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/108.xml
new file mode 100644
index 0000000000000000000000000000000000000000..fd46a3df4dea17af6e9cc0341e47b4caa4151dfe
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/108.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>108.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\108.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>395</width>
+		<height>594</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>49</xmin>
+			<ymin>37</ymin>
+			<xmax>353</xmax>
+			<ymax>587</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/109.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/109.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/109.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/109.xml
new file mode 100644
index 0000000000000000000000000000000000000000..7c8b5113f509fd1910898a775309dbd7e37b5a93
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/109.xml	
@@ -0,0 +1,98 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>109.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\109.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>507</width>
+		<height>338</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>298</xmin>
+			<ymin>224</ymin>
+			<xmax>333</xmax>
+			<ymax>273</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>246</xmin>
+			<ymin>225</ymin>
+			<xmax>275</xmax>
+			<ymax>274</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>186</xmin>
+			<ymin>224</ymin>
+			<xmax>219</xmax>
+			<ymax>273</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>134</xmin>
+			<ymin>226</ymin>
+			<xmax>168</xmax>
+			<ymax>269</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>348</xmin>
+			<ymin>226</ymin>
+			<xmax>377</xmax>
+			<ymax>270</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>379</xmin>
+			<ymin>226</ymin>
+			<xmax>407</xmax>
+			<ymax>269</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>99</xmin>
+			<ymin>225</ymin>
+			<xmax>131</xmax>
+			<ymax>267</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/11.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/11.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/11.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/11.xml
new file mode 100644
index 0000000000000000000000000000000000000000..4f2ab8385285dd4e1750c7607e44353d17e93df8
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/11.xml	
@@ -0,0 +1,134 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>11.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\11.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>508</width>
+		<height>338</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>400</xmin>
+			<ymin>20</ymin>
+			<xmax>508</xmax>
+			<ymax>255</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>294</xmin>
+			<ymin>19</ymin>
+			<xmax>405</xmax>
+			<ymax>205</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>226</xmin>
+			<ymin>18</ymin>
+			<xmax>312</xmax>
+			<ymax>172</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>164</xmin>
+			<ymin>15</ymin>
+			<xmax>243</xmax>
+			<ymax>148</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>107</xmin>
+			<ymin>12</ymin>
+			<xmax>172</xmax>
+			<ymax>125</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>84</xmin>
+			<ymin>13</ymin>
+			<xmax>135</xmax>
+			<ymax>114</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>58</xmin>
+			<ymin>11</ymin>
+			<xmax>99</xmax>
+			<ymax>102</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>310</xmin>
+			<ymin>7</ymin>
+			<xmax>341</xmax>
+			<ymax>64</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>423</xmin>
+			<ymin>8</ymin>
+			<xmax>464</xmax>
+			<ymax>79</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>171</xmin>
+			<ymin>8</ymin>
+			<xmax>194</xmax>
+			<ymax>46</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/110.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/110.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/110.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/110.xml
new file mode 100644
index 0000000000000000000000000000000000000000..ca3674f25b2a0174db035fe033d5cf1191c0bf64
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/110.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>110.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\110.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>413</width>
+		<height>416</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>109</xmin>
+			<ymin>61</ymin>
+			<xmax>269</xmax>
+			<ymax>324</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/111.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/111.jpg
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Binary files /dev/null and b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/111.jpg differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/111.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/111.xml
new file mode 100644
index 0000000000000000000000000000000000000000..c193e113c287d37b2e3e40ae61d3e357e2c7b69e
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/111.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>111.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\111.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>359</width>
+		<height>478</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>1</xmin>
+			<ymin>18</ymin>
+			<xmax>148</xmax>
+			<ymax>270</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/112.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/112.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/112.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/112.xml
new file mode 100644
index 0000000000000000000000000000000000000000..6b5ae328e4255250e9da0f97637d182e5bdaa2dd
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/112.xml	
@@ -0,0 +1,38 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>112.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\112.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>507</width>
+		<height>337</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>233</xmin>
+			<ymin>186</ymin>
+			<xmax>303</xmax>
+			<ymax>304</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>31</xmin>
+			<ymin>194</ymin>
+			<xmax>108</xmax>
+			<ymax>312</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/113.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/113.jpg
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Binary files /dev/null and b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/113.jpg differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/113.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/113.xml
new file mode 100644
index 0000000000000000000000000000000000000000..f215a8926395304615b57553496ff2916527559e
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/113.xml	
@@ -0,0 +1,38 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>113.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\113.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>507</width>
+		<height>337</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>166</xmin>
+			<ymin>210</ymin>
+			<xmax>206</xmax>
+			<ymax>273</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>52</xmin>
+			<ymin>212</ymin>
+			<xmax>95</xmax>
+			<ymax>275</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/114.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/114.jpg
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Binary files /dev/null and b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/114.jpg differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/114.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/114.xml
new file mode 100644
index 0000000000000000000000000000000000000000..63c7af1c30678797a8487a0db6c79f503546bbf2
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/114.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>114.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\114.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>359</width>
+		<height>478</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>113</xmin>
+			<ymin>207</ymin>
+			<xmax>280</xmax>
+			<ymax>461</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/115.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/115.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/115.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/115.xml
new file mode 100644
index 0000000000000000000000000000000000000000..bf71ccf9a473f81f5cb5cd4938f212e8ddfbd312
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/115.xml	
@@ -0,0 +1,74 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>115.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\115.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>507</width>
+		<height>338</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>384</xmin>
+			<ymin>235</ymin>
+			<xmax>439</xmax>
+			<ymax>316</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>459</xmin>
+			<ymin>234</ymin>
+			<xmax>507</xmax>
+			<ymax>315</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>172</xmin>
+			<ymin>213</ymin>
+			<xmax>197</xmax>
+			<ymax>262</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>40</xmin>
+			<ymin>194</ymin>
+			<xmax>61</xmax>
+			<ymax>230</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>144</xmin>
+			<ymin>209</ymin>
+			<xmax>173</xmax>
+			<ymax>259</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/116.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/116.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/116.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/116.xml
new file mode 100644
index 0000000000000000000000000000000000000000..ccb5344abb84b37663afcb63de06581f18f280e3
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/116.xml	
@@ -0,0 +1,74 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>116.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\116.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>416</width>
+		<height>412</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>193</xmin>
+			<ymin>216</ymin>
+			<xmax>281</xmax>
+			<ymax>397</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>139</xmin>
+			<ymin>183</ymin>
+			<xmax>217</xmax>
+			<ymax>331</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>245</xmin>
+			<ymin>138</ymin>
+			<xmax>334</xmax>
+			<ymax>182</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>132</xmin>
+			<ymin>117</ymin>
+			<xmax>186</xmax>
+			<ymax>236</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>188</xmin>
+			<ymin>114</ymin>
+			<xmax>241</xmax>
+			<ymax>210</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/117.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/117.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/117.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/117.xml
new file mode 100644
index 0000000000000000000000000000000000000000..2f72f24c9191dcead939676baa69cd3f7f328c9b
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/117.xml	
@@ -0,0 +1,74 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>117.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\117.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>478</width>
+		<height>359</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>156</xmin>
+			<ymin>83</ymin>
+			<xmax>255</xmax>
+			<ymax>278</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>82</xmin>
+			<ymin>36</ymin>
+			<xmax>170</xmax>
+			<ymax>206</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>256</xmin>
+			<ymin>74</ymin>
+			<xmax>359</xmax>
+			<ymax>251</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>158</xmin>
+			<ymin>14</ymin>
+			<xmax>241</xmax>
+			<ymax>160</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>246</xmin>
+			<ymin>28</ymin>
+			<xmax>338</xmax>
+			<ymax>175</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/118.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/118.jpg
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Binary files /dev/null and b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/118.jpg differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/118.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/118.xml
new file mode 100644
index 0000000000000000000000000000000000000000..68e0cefa006ddf1881ea87bedb7a289fe153eeb3
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/118.xml	
@@ -0,0 +1,74 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>118.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\118.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>1024</width>
+		<height>665</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>671</xmin>
+			<ymin>439</ymin>
+			<xmax>773</xmax>
+			<ymax>587</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>537</xmin>
+			<ymin>440</ymin>
+			<xmax>653</xmax>
+			<ymax>588</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>346</xmin>
+			<ymin>442</ymin>
+			<xmax>457</xmax>
+			<ymax>572</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>95</xmin>
+			<ymin>445</ymin>
+			<xmax>214</xmax>
+			<ymax>565</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>1</xmin>
+			<ymin>442</ymin>
+			<xmax>59</xmax>
+			<ymax>553</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/119.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/119.jpg
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Binary files /dev/null and b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/119.jpg differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/119.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/119.xml
new file mode 100644
index 0000000000000000000000000000000000000000..4f383288670b2a8db0cd41847b974c8dfc09fc78
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/119.xml	
@@ -0,0 +1,38 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>119.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\119.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>509</width>
+		<height>339</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>73</xmin>
+			<ymin>197</ymin>
+			<xmax>127</xmax>
+			<ymax>303</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>101</xmin>
+			<ymin>188</ymin>
+			<xmax>151</xmax>
+			<ymax>276</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/12.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/12.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/12.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/12.xml
new file mode 100644
index 0000000000000000000000000000000000000000..9f58bcc29b2a73e0c6219b1587ee49c1d6b9bb4c
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/12.xml	
@@ -0,0 +1,50 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>12.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\12.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>359</width>
+		<height>479</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>121</xmin>
+			<ymin>115</ymin>
+			<xmax>340</xmax>
+			<ymax>426</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>104</xmin>
+			<ymin>175</ymin>
+			<xmax>192</xmax>
+			<ymax>314</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>74</xmin>
+			<ymin>196</ymin>
+			<xmax>118</xmax>
+			<ymax>289</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/120.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/120.jpg
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Binary files /dev/null and b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/120.jpg differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/120.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/120.xml
new file mode 100644
index 0000000000000000000000000000000000000000..1c0cab18d7ae5a65a8e117f40bedd15886b4bf49
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/120.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>120.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\120.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>506</width>
+		<height>337</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>264</xmin>
+			<ymin>213</ymin>
+			<xmax>314</xmax>
+			<ymax>308</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/121.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/121.jpg
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Binary files /dev/null and b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/121.jpg differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/121.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/121.xml
new file mode 100644
index 0000000000000000000000000000000000000000..944761234557b7ea96673f2349f6a8b12108ca93
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/121.xml	
@@ -0,0 +1,86 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>121.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\121.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>507</width>
+		<height>337</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>84</xmin>
+			<ymin>150</ymin>
+			<xmax>175</xmax>
+			<ymax>337</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>214</xmin>
+			<ymin>117</ymin>
+			<xmax>233</xmax>
+			<ymax>153</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>281</xmin>
+			<ymin>121</ymin>
+			<xmax>314</xmax>
+			<ymax>174</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>259</xmin>
+			<ymin>117</ymin>
+			<xmax>283</xmax>
+			<ymax>159</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>235</xmin>
+			<ymin>118</ymin>
+			<xmax>252</xmax>
+			<ymax>151</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>186</xmin>
+			<ymin>117</ymin>
+			<xmax>199</xmax>
+			<ymax>137</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/122.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/122.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/122.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/122.xml
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index 0000000000000000000000000000000000000000..b9d947a59c2202afa2f9fb04dd505affde1fb881
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/122.xml	
@@ -0,0 +1,38 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>122.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\122.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>339</width>
+		<height>510</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>145</xmin>
+			<ymin>377</ymin>
+			<xmax>186</xmax>
+			<ymax>445</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>251</xmin>
+			<ymin>357</ymin>
+			<xmax>263</xmax>
+			<ymax>392</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/123.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/123.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/123.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/123.xml
new file mode 100644
index 0000000000000000000000000000000000000000..0eb8bd7ef2d788a9d84a48dac0cb01ec3e2a1c28
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/123.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>123.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\123.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>508</width>
+		<height>337</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>51</xmin>
+			<ymin>44</ymin>
+			<xmax>123</xmax>
+			<ymax>240</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/124.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/124.jpg
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Binary files /dev/null and b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/124.jpg differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/124.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/124.xml
new file mode 100644
index 0000000000000000000000000000000000000000..b5fe822327a216ca72025717584bca76c40d97b1
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/124.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>124.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\124.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>359</width>
+		<height>479</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>125</xmin>
+			<ymin>259</ymin>
+			<xmax>225</xmax>
+			<ymax>397</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/125.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/125.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/125.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/125.xml
new file mode 100644
index 0000000000000000000000000000000000000000..245b581fc7f24b23d4a358f830bea9315307d9fc
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/125.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>125.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\125.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>507</width>
+		<height>338</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>195</xmin>
+			<ymin>93</ymin>
+			<xmax>309</xmax>
+			<ymax>264</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/126.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/126.jpg
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Binary files /dev/null and b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/126.jpg differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/126.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/126.xml
new file mode 100644
index 0000000000000000000000000000000000000000..1d15e09d77114e104f70ae17f1e1ccabdc3f98d1
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/126.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>126.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\126.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>338</width>
+		<height>507</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>12</xmin>
+			<ymin>31</ymin>
+			<xmax>317</xmax>
+			<ymax>477</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/127.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/127.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/127.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/127.xml
new file mode 100644
index 0000000000000000000000000000000000000000..53f02dd33dc696122921082e84b4b2bc94b896b5
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/127.xml	
@@ -0,0 +1,122 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>127.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\127.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>529</width>
+		<height>327</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>50</xmin>
+			<ymin>154</ymin>
+			<xmax>154</xmax>
+			<ymax>320</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>147</xmin>
+			<ymin>125</ymin>
+			<xmax>241</xmax>
+			<ymax>279</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>225</xmin>
+			<ymin>104</ymin>
+			<xmax>312</xmax>
+			<ymax>250</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>286</xmin>
+			<ymin>87</ymin>
+			<xmax>367</xmax>
+			<ymax>226</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>339</xmin>
+			<ymin>73</ymin>
+			<xmax>408</xmax>
+			<ymax>202</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>379</xmin>
+			<ymin>59</ymin>
+			<xmax>445</xmax>
+			<ymax>185</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>414</xmin>
+			<ymin>46</ymin>
+			<xmax>478</xmax>
+			<ymax>163</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>459</xmin>
+			<ymin>31</ymin>
+			<xmax>519</xmax>
+			<ymax>145</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>497</xmin>
+			<ymin>21</ymin>
+			<xmax>529</xmax>
+			<ymax>127</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/128.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/128.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/128.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/128.xml
new file mode 100644
index 0000000000000000000000000000000000000000..fab7b12143c8c8b859dd49a6354cee9bab868c80
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/128.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>128.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\128.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>339</width>
+		<height>506</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>69</xmin>
+			<ymin>52</ymin>
+			<xmax>296</xmax>
+			<ymax>462</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/129.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/129.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/129.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/129.xml
new file mode 100644
index 0000000000000000000000000000000000000000..ad637f0742b7c7f9f8b3d9d7b89c664781a70572
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/129.xml	
@@ -0,0 +1,74 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>129.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\129.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>509</width>
+		<height>339</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>314</xmin>
+			<ymin>202</ymin>
+			<xmax>386</xmax>
+			<ymax>318</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>93</xmin>
+			<ymin>207</ymin>
+			<xmax>158</xmax>
+			<ymax>313</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>403</xmin>
+			<ymin>217</ymin>
+			<xmax>446</xmax>
+			<ymax>285</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>276</xmin>
+			<ymin>215</ymin>
+			<xmax>322</xmax>
+			<ymax>291</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>17</xmin>
+			<ymin>223</ymin>
+			<xmax>47</xmax>
+			<ymax>273</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/13.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/13.jpg
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Binary files /dev/null and b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/13.jpg differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/13.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/13.xml
new file mode 100644
index 0000000000000000000000000000000000000000..fab6df7a28fc5d5b6c3a24e81fe756167fa26c62
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/13.xml	
@@ -0,0 +1,62 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>13.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\13.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>414</width>
+		<height>414</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>233</xmin>
+			<ymin>132</ymin>
+			<xmax>374</xmax>
+			<ymax>368</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>154</xmin>
+			<ymin>95</ymin>
+			<xmax>258</xmax>
+			<ymax>270</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>89</xmin>
+			<ymin>79</ymin>
+			<xmax>176</xmax>
+			<ymax>219</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>33</xmin>
+			<ymin>65</ymin>
+			<xmax>108</xmax>
+			<ymax>183</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/130.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/130.jpg
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Binary files /dev/null and b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/130.jpg differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/130.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/130.xml
new file mode 100644
index 0000000000000000000000000000000000000000..9949338f42a422895792c5c8759efe6b463f8c8a
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/130.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>130.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\130.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>396</width>
+		<height>430</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>143</xmin>
+			<ymin>48</ymin>
+			<xmax>305</xmax>
+			<ymax>280</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/131.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/131.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/131.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/131.xml
new file mode 100644
index 0000000000000000000000000000000000000000..57720ed77f73671baeeda38413d6433a2e4a450c
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/131.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>131.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\131.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>375</width>
+		<height>458</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>71</xmin>
+			<ymin>40</ymin>
+			<xmax>296</xmax>
+			<ymax>407</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/132.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/132.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/132.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/132.xml
new file mode 100644
index 0000000000000000000000000000000000000000..94d0d12a648cdf7994145766520f81a43f8f7d9b
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/132.xml	
@@ -0,0 +1,38 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>132.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\132.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>507</width>
+		<height>338</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>60</xmin>
+			<ymin>63</ymin>
+			<xmax>198</xmax>
+			<ymax>300</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>177</xmin>
+			<ymin>139</ymin>
+			<xmax>380</xmax>
+			<ymax>300</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/133.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/133.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/133.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/133.xml
new file mode 100644
index 0000000000000000000000000000000000000000..286952000d06ca9843d44ac6927b25023ccbbd99
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/133.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>133.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\133.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>414</width>
+		<height>414</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>65</xmin>
+			<ymin>9</ymin>
+			<xmax>343</xmax>
+			<ymax>399</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/134.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/134.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/134.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/134.xml
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/134.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>134.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\134.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>414</width>
+		<height>414</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>87</xmin>
+			<ymin>6</ymin>
+			<xmax>331</xmax>
+			<ymax>409</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/135.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/135.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/135.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/135.xml
new file mode 100644
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/135.xml	
@@ -0,0 +1,50 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>135.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\135.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>478</width>
+		<height>359</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>142</xmin>
+			<ymin>133</ymin>
+			<xmax>285</xmax>
+			<ymax>303</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>323</xmin>
+			<ymin>84</ymin>
+			<xmax>443</xmax>
+			<ymax>213</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>40</xmin>
+			<ymin>111</ymin>
+			<xmax>171</xmax>
+			<ymax>245</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/136.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/136.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/136.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/136.xml
new file mode 100644
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/136.xml	
@@ -0,0 +1,110 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>136.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\136.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>837</width>
+		<height>1024</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>485</xmin>
+			<ymin>480</ymin>
+			<xmax>568</xmax>
+			<ymax>621</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>570</xmin>
+			<ymin>412</ymin>
+			<xmax>639</xmax>
+			<ymax>528</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>644</xmin>
+			<ymin>361</ymin>
+			<xmax>704</xmax>
+			<ymax>451</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>714</xmin>
+			<ymin>308</ymin>
+			<xmax>764</xmax>
+			<ymax>385</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>773</xmin>
+			<ymin>273</ymin>
+			<xmax>817</xmax>
+			<ymax>334</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>264</xmin>
+			<ymin>615</ymin>
+			<xmax>390</xmax>
+			<ymax>848</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>126</xmin>
+			<ymin>707</ymin>
+			<xmax>284</xmax>
+			<ymax>985</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>391</xmin>
+			<ymin>642</ymin>
+			<xmax>536</xmax>
+			<ymax>766</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/137.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/137.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/137.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/137.xml
new file mode 100644
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/137.xml	
@@ -0,0 +1,50 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>137.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\137.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>507</width>
+		<height>338</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>117</xmin>
+			<ymin>168</ymin>
+			<xmax>303</xmax>
+			<ymax>293</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>349</xmin>
+			<ymin>90</ymin>
+			<xmax>492</xmax>
+			<ymax>297</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>283</xmin>
+			<ymin>100</ymin>
+			<xmax>365</xmax>
+			<ymax>259</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/138.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/138.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/138.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/138.xml
new file mode 100644
index 0000000000000000000000000000000000000000..161be36dfd6b0e9ec41456dd28229e5ae5a9aa3f
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/138.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>138.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\138.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>381</width>
+		<height>454</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>6</xmin>
+			<ymin>20</ymin>
+			<xmax>367</xmax>
+			<ymax>434</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/139.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/139.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/139.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/139.xml
new file mode 100644
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/139.xml	
@@ -0,0 +1,62 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>139.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\139.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>340</width>
+		<height>508</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>17</xmin>
+			<ymin>264</ymin>
+			<xmax>120</xmax>
+			<ymax>460</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>4</xmin>
+			<ymin>251</ymin>
+			<xmax>55</xmax>
+			<ymax>385</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>208</xmin>
+			<ymin>262</ymin>
+			<xmax>314</xmax>
+			<ymax>460</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>269</xmin>
+			<ymin>244</ymin>
+			<xmax>338</xmax>
+			<ymax>379</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/14.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/14.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/14.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/14.xml
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/14.xml	
@@ -0,0 +1,50 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>14.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\14.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>359</width>
+		<height>479</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>118</xmin>
+			<ymin>113</ymin>
+			<xmax>338</xmax>
+			<ymax>423</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>95</xmin>
+			<ymin>175</ymin>
+			<xmax>190</xmax>
+			<ymax>313</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>78</xmin>
+			<ymin>196</ymin>
+			<xmax>120</xmax>
+			<ymax>283</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/140.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/140.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/140.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/140.xml
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index 0000000000000000000000000000000000000000..80e5d6637db504bc0d76836d7136b1569ee4ac43
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/140.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>140.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\140.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>337</width>
+		<height>506</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>183</xmin>
+			<ymin>166</ymin>
+			<xmax>314</xmax>
+			<ymax>388</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/141.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/141.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/141.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/141.xml
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/141.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>141.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\141.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>337</width>
+		<height>508</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>49</xmin>
+			<ymin>45</ymin>
+			<xmax>260</xmax>
+			<ymax>459</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/142.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/142.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/142.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/142.xml
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/142.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>142.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\142.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>337</width>
+		<height>506</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>145</xmin>
+			<ymin>291</ymin>
+			<xmax>192</xmax>
+			<ymax>369</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/143.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/143.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/143.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/143.xml
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/143.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>143.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\143.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>338</width>
+		<height>507</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>130</xmin>
+			<ymin>155</ymin>
+			<xmax>304</xmax>
+			<ymax>477</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/144.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/144.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/144.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/144.xml
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/144.xml	
@@ -0,0 +1,62 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>144.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\144.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>416</width>
+		<height>416</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>158</xmin>
+			<ymin>178</ymin>
+			<xmax>203</xmax>
+			<ymax>247</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>165</xmin>
+			<ymin>339</ymin>
+			<xmax>230</xmax>
+			<ymax>416</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>158</xmin>
+			<ymin>154</ymin>
+			<xmax>184</xmax>
+			<ymax>196</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>186</xmin>
+			<ymin>139</ymin>
+			<xmax>196</xmax>
+			<ymax>153</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/145.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/145.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/145.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/145.xml
new file mode 100644
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/145.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>145.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\145.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>506</width>
+		<height>341</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>273</xmin>
+			<ymin>45</ymin>
+			<xmax>442</xmax>
+			<ymax>337</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/146.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/146.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/146.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/146.xml
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/146.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>146.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\146.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>336</width>
+		<height>509</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>80</xmin>
+			<ymin>119</ymin>
+			<xmax>336</xmax>
+			<ymax>509</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/147.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/147.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/147.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/147.xml
new file mode 100644
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/147.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>147.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\147.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>347</width>
+		<height>491</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>40</xmin>
+			<ymin>126</ymin>
+			<xmax>240</xmax>
+			<ymax>450</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/148.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/148.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/148.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/148.xml
new file mode 100644
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/148.xml	
@@ -0,0 +1,38 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>148.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\148.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>507</width>
+		<height>337</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>10</xmin>
+			<ymin>16</ymin>
+			<xmax>175</xmax>
+			<ymax>327</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>339</xmin>
+			<ymin>18</ymin>
+			<xmax>507</xmax>
+			<ymax>328</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/149.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/149.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/149.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/149.xml
new file mode 100644
index 0000000000000000000000000000000000000000..cca92371bac194de8c02bf0f9096e1d625a62a31
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/149.xml	
@@ -0,0 +1,50 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>149.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\149.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>509</width>
+		<height>339</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>10</xmin>
+			<ymin>100</ymin>
+			<xmax>94</xmax>
+			<ymax>216</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>131</xmin>
+			<ymin>104</ymin>
+			<xmax>221</xmax>
+			<ymax>214</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>483</xmin>
+			<ymin>140</ymin>
+			<xmax>509</xmax>
+			<ymax>212</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/15.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/15.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/15.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/15.xml
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/15.xml	
@@ -0,0 +1,38 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>15.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\15.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>478</width>
+		<height>359</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>186</xmin>
+			<ymin>49</ymin>
+			<xmax>357</xmax>
+			<ymax>287</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>381</xmin>
+			<ymin>166</ymin>
+			<xmax>449</xmax>
+			<ymax>263</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/150.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/150.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/150.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/150.xml
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index 0000000000000000000000000000000000000000..2995b80b8d861da6272db85aa08ee34553c693f5
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/150.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>150.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\150.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>359</width>
+		<height>479</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>112</xmin>
+			<ymin>97</ymin>
+			<xmax>244</xmax>
+			<ymax>345</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/151.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/151.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/151.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/151.xml
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/151.xml	
@@ -0,0 +1,62 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>151.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\151.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>479</width>
+		<height>359</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>190</xmin>
+			<ymin>89</ymin>
+			<xmax>270</xmax>
+			<ymax>217</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>236</xmin>
+			<ymin>36</ymin>
+			<xmax>289</xmax>
+			<ymax>123</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>73</xmin>
+			<ymin>244</ymin>
+			<xmax>146</xmax>
+			<ymax>359</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>249</xmin>
+			<ymin>11</ymin>
+			<xmax>291</xmax>
+			<ymax>77</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/152.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/152.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/152.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/152.xml
new file mode 100644
index 0000000000000000000000000000000000000000..d0171f2627166e579829b798383327e9d0931963
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/152.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>152.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\152.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>509</width>
+		<height>339</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>274</xmin>
+			<ymin>57</ymin>
+			<xmax>391</xmax>
+			<ymax>196</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/153.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/153.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/153.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/153.xml
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/153.xml	
@@ -0,0 +1,74 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>153.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\153.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>509</width>
+		<height>339</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>91</xmin>
+			<ymin>172</ymin>
+			<xmax>172</xmax>
+			<ymax>325</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>319</xmin>
+			<ymin>174</ymin>
+			<xmax>422</xmax>
+			<ymax>333</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>422</xmin>
+			<ymin>156</ymin>
+			<xmax>452</xmax>
+			<ymax>207</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>339</xmin>
+			<ymin>152</ymin>
+			<xmax>374</xmax>
+			<ymax>207</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>487</xmin>
+			<ymin>156</ymin>
+			<xmax>509</xmax>
+			<ymax>204</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/154.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/154.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/154.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/154.xml
new file mode 100644
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/154.xml	
@@ -0,0 +1,50 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>154.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\154.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>359</width>
+		<height>479</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>110</xmin>
+			<ymin>68</ymin>
+			<xmax>178</xmax>
+			<ymax>179</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>217</xmin>
+			<ymin>180</ymin>
+			<xmax>340</xmax>
+			<ymax>299</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>135</xmin>
+			<ymin>143</ymin>
+			<xmax>224</xmax>
+			<ymax>244</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/155.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/155.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/155.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/155.xml
new file mode 100644
index 0000000000000000000000000000000000000000..82bc0d357a80d7177d2a19235eddea2f11f8b47b
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/155.xml	
@@ -0,0 +1,62 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>155.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\155.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>479</width>
+		<height>359</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>120</xmin>
+			<ymin>67</ymin>
+			<xmax>222</xmax>
+			<ymax>253</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>1</xmin>
+			<ymin>59</ymin>
+			<xmax>102</xmax>
+			<ymax>256</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>296</xmin>
+			<ymin>77</ymin>
+			<xmax>400</xmax>
+			<ymax>253</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>392</xmin>
+			<ymin>74</ymin>
+			<xmax>479</xmax>
+			<ymax>260</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/156.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/156.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/156.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/156.xml
new file mode 100644
index 0000000000000000000000000000000000000000..80b4b0b170dcf0594ab1c5fb073c9121da2887fc
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/156.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>156.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\156.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>508</width>
+		<height>340</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>207</xmin>
+			<ymin>93</ymin>
+			<xmax>315</xmax>
+			<ymax>266</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/157.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/157.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/157.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/157.xml
new file mode 100644
index 0000000000000000000000000000000000000000..c523b4e61c43637dd169408366a01f17565cc314
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/157.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>157.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\157.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>552</width>
+		<height>312</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>47</xmin>
+			<ymin>1</ymin>
+			<xmax>313</xmax>
+			<ymax>265</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/158.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/158.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/158.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/158.xml
new file mode 100644
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/158.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>158.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\158.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>1023</width>
+		<height>1024</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>683</xmin>
+			<ymin>420</ymin>
+			<xmax>965</xmax>
+			<ymax>934</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/159.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/159.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/159.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/159.xml
new file mode 100644
index 0000000000000000000000000000000000000000..c72ca86fe18915da04d2312730fdf0d89cf6ba6a
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/159.xml	
@@ -0,0 +1,86 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>159.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\159.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>339</width>
+		<height>508</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>52</xmin>
+			<ymin>243</ymin>
+			<xmax>150</xmax>
+			<ymax>460</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>172</xmin>
+			<ymin>184</ymin>
+			<xmax>237</xmax>
+			<ymax>315</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>230</xmin>
+			<ymin>152</ymin>
+			<xmax>270</xmax>
+			<ymax>235</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>257</xmin>
+			<ymin>139</ymin>
+			<xmax>285</xmax>
+			<ymax>203</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>279</xmin>
+			<ymin>128</ymin>
+			<xmax>300</xmax>
+			<ymax>180</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>290</xmin>
+			<ymin>126</ymin>
+			<xmax>307</xmax>
+			<ymax>165</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/16.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/16.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/16.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/16.xml
new file mode 100644
index 0000000000000000000000000000000000000000..1d7ea701eadfbe5c49172c2da5e89e8bfb6d2b93
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/16.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>16.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\16.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>478</width>
+		<height>359</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>39</xmin>
+			<ymin>101</ymin>
+			<xmax>215</xmax>
+			<ymax>332</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/160.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/160.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/160.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/160.xml
new file mode 100644
index 0000000000000000000000000000000000000000..088a0165cf948d1d1f5e8cab48e0295fdcb36244
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/160.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>160.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\160.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>479</width>
+		<height>359</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>147</xmin>
+			<ymin>7</ymin>
+			<xmax>258</xmax>
+			<ymax>161</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/161.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/161.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/161.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/161.xml
new file mode 100644
index 0000000000000000000000000000000000000000..4cf83e77f85bfbfbd18a977104831a97931cb6ff
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/161.xml	
@@ -0,0 +1,254 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>161.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\161.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>508</width>
+		<height>339</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>206</xmin>
+			<ymin>125</ymin>
+			<xmax>321</xmax>
+			<ymax>315</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>41</xmin>
+			<ymin>126</ymin>
+			<xmax>139</xmax>
+			<ymax>322</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>154</xmin>
+			<ymin>89</ymin>
+			<xmax>233</xmax>
+			<ymax>213</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>25</xmin>
+			<ymin>89</ymin>
+			<xmax>74</xmax>
+			<ymax>213</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>71</xmin>
+			<ymin>50</ymin>
+			<xmax>119</xmax>
+			<ymax>128</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>1</xmin>
+			<ymin>45</ymin>
+			<xmax>53</xmax>
+			<ymax>119</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>394</xmin>
+			<ymin>121</ymin>
+			<xmax>508</xmax>
+			<ymax>308</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>467</xmin>
+			<ymin>88</ymin>
+			<xmax>508</xmax>
+			<ymax>202</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>346</xmin>
+			<ymin>75</ymin>
+			<xmax>413</xmax>
+			<ymax>182</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>269</xmin>
+			<ymin>66</ymin>
+			<xmax>320</xmax>
+			<ymax>162</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>199</xmin>
+			<ymin>62</ymin>
+			<xmax>252</xmax>
+			<ymax>148</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>136</xmin>
+			<ymin>55</ymin>
+			<xmax>191</xmax>
+			<ymax>136</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>109</xmin>
+			<ymin>19</ymin>
+			<xmax>136</xmax>
+			<ymax>69</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>65</xmin>
+			<ymin>18</ymin>
+			<xmax>91</xmax>
+			<ymax>69</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>24</xmin>
+			<ymin>19</ymin>
+			<xmax>52</xmax>
+			<ymax>69</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>227</xmin>
+			<ymin>22</ymin>
+			<xmax>256</xmax>
+			<ymax>72</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>185</xmin>
+			<ymin>20</ymin>
+			<xmax>213</xmax>
+			<ymax>73</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>367</xmin>
+			<ymin>24</ymin>
+			<xmax>396</xmax>
+			<ymax>77</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>413</xmin>
+			<ymin>23</ymin>
+			<xmax>448</xmax>
+			<ymax>77</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>468</xmin>
+			<ymin>23</ymin>
+			<xmax>501</xmax>
+			<ymax>80</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/162.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/162.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/162.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/162.xml
new file mode 100644
index 0000000000000000000000000000000000000000..a9a71716e4d3ae0c327b3a79fb905601a9d949ed
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/162.xml	
@@ -0,0 +1,110 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>162.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\162.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>339</width>
+		<height>509</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>17</xmin>
+			<ymin>254</ymin>
+			<xmax>57</xmax>
+			<ymax>330</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>60</xmin>
+			<ymin>254</ymin>
+			<xmax>102</xmax>
+			<ymax>332</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>105</xmin>
+			<ymin>252</ymin>
+			<xmax>145</xmax>
+			<ymax>331</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>142</xmin>
+			<ymin>252</ymin>
+			<xmax>186</xmax>
+			<ymax>326</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>186</xmin>
+			<ymin>249</ymin>
+			<xmax>227</xmax>
+			<ymax>325</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>230</xmin>
+			<ymin>248</ymin>
+			<xmax>268</xmax>
+			<ymax>326</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>261</xmin>
+			<ymin>251</ymin>
+			<xmax>305</xmax>
+			<ymax>325</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>300</xmin>
+			<ymin>250</ymin>
+			<xmax>338</xmax>
+			<ymax>321</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/163.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/163.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/163.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/163.xml
new file mode 100644
index 0000000000000000000000000000000000000000..bbb8815c44081a3f5f46509800216c10ce99cff7
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/163.xml	
@@ -0,0 +1,86 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>163.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\163.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>339</width>
+		<height>509</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>153</xmin>
+			<ymin>404</ymin>
+			<xmax>194</xmax>
+			<ymax>486</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>115</xmin>
+			<ymin>399</ymin>
+			<xmax>152</xmax>
+			<ymax>479</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>187</xmin>
+			<ymin>402</ymin>
+			<xmax>230</xmax>
+			<ymax>480</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>213</xmin>
+			<ymin>401</ymin>
+			<xmax>251</xmax>
+			<ymax>470</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>95</xmin>
+			<ymin>401</ymin>
+			<xmax>122</xmax>
+			<ymax>464</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>185</xmin>
+			<ymin>385</ymin>
+			<xmax>205</xmax>
+			<ymax>445</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/164.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/164.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/164.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/164.xml
new file mode 100644
index 0000000000000000000000000000000000000000..5a62d5b830c0c9e5e7ee205893ed390ad94b3ea3
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/164.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>164.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\164.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>359</width>
+		<height>479</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>201</xmin>
+			<ymin>144</ymin>
+			<xmax>331</xmax>
+			<ymax>406</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/165.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/165.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/165.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/165.xml
new file mode 100644
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/165.xml	
@@ -0,0 +1,98 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>165.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\165.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>509</width>
+		<height>339</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>200</xmin>
+			<ymin>59</ymin>
+			<xmax>239</xmax>
+			<ymax>156</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>90</xmin>
+			<ymin>175</ymin>
+			<xmax>162</xmax>
+			<ymax>337</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>245</xmin>
+			<ymin>21</ymin>
+			<xmax>271</xmax>
+			<ymax>76</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>456</xmin>
+			<ymin>112</ymin>
+			<xmax>494</xmax>
+			<ymax>234</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>392</xmin>
+			<ymin>50</ymin>
+			<xmax>426</xmax>
+			<ymax>131</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>356</xmin>
+			<ymin>12</ymin>
+			<xmax>377</xmax>
+			<ymax>61</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>344</xmin>
+			<ymin>1</ymin>
+			<xmax>357</xmax>
+			<ymax>32</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/166.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/166.jpg
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Binary files /dev/null and b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/166.jpg differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/166.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/166.xml
new file mode 100644
index 0000000000000000000000000000000000000000..c6fe5e9e0ef2e6be4efeb145aadc3a7c25437837
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/166.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>166.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\166.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>509</width>
+		<height>339</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>273</xmin>
+			<ymin>32</ymin>
+			<xmax>396</xmax>
+			<ymax>243</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/167.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/167.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/167.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/167.xml
new file mode 100644
index 0000000000000000000000000000000000000000..5494f8e6c259de10b84ac6e6316eb818d701e5f4
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/167.xml	
@@ -0,0 +1,62 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>167.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\167.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>512</width>
+		<height>336</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>7</xmin>
+			<ymin>150</ymin>
+			<xmax>95</xmax>
+			<ymax>298</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>136</xmin>
+			<ymin>152</ymin>
+			<xmax>205</xmax>
+			<ymax>302</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>338</xmin>
+			<ymin>155</ymin>
+			<xmax>416</xmax>
+			<ymax>300</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>412</xmin>
+			<ymin>158</ymin>
+			<xmax>496</xmax>
+			<ymax>297</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/168.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/168.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/168.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/168.xml
new file mode 100644
index 0000000000000000000000000000000000000000..ee3ba82bcba8909d3e316f20a573f9dd3b938c52
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/168.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>168.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\168.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>768</width>
+		<height>1024</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>273</xmin>
+			<ymin>421</ymin>
+			<xmax>471</xmax>
+			<ymax>901</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/169.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/169.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/169.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/169.xml
new file mode 100644
index 0000000000000000000000000000000000000000..a746a6ab81a0f3dc3a075134069c968eb5fda4fc
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/169.xml	
@@ -0,0 +1,50 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>169.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\169.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>359</width>
+		<height>479</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>60</xmin>
+			<ymin>304</ymin>
+			<xmax>174</xmax>
+			<ymax>474</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>160</xmin>
+			<ymin>217</ymin>
+			<xmax>246</xmax>
+			<ymax>356</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>238</xmin>
+			<ymin>164</ymin>
+			<xmax>320</xmax>
+			<ymax>296</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/17.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/17.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/17.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/17.xml
new file mode 100644
index 0000000000000000000000000000000000000000..0917b58198ec496f543687e542bc21fee5f174e0
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/17.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>17.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\17.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>338</width>
+		<height>507</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>63</xmin>
+			<ymin>14</ymin>
+			<xmax>294</xmax>
+			<ymax>490</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/170.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/170.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/170.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/170.xml
new file mode 100644
index 0000000000000000000000000000000000000000..93e0899c02040dfe35e4c9bbb47cf8290127bc8a
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/170.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>170.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\170.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>359</width>
+		<height>479</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>237</xmin>
+			<ymin>169</ymin>
+			<xmax>296</xmax>
+			<ymax>283</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/171.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/171.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/171.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/171.xml
new file mode 100644
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/171.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>171.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\171.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>359</width>
+		<height>479</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>206</xmin>
+			<ymin>140</ymin>
+			<xmax>336</xmax>
+			<ymax>413</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/172.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/172.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/172.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/172.xml
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/172.xml	
@@ -0,0 +1,38 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>172.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\172.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>359</width>
+		<height>479</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>204</xmin>
+			<ymin>343</ymin>
+			<xmax>298</xmax>
+			<ymax>477</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>71</xmin>
+			<ymin>2</ymin>
+			<xmax>105</xmax>
+			<ymax>47</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/173.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/173.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/173.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/173.xml
new file mode 100644
index 0000000000000000000000000000000000000000..ee6b1c23b4ce4d5606f0fd6fcd31d829b23c22ec
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/173.xml	
@@ -0,0 +1,38 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>173.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\173.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>557</width>
+		<height>311</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>61</xmin>
+			<ymin>52</ymin>
+			<xmax>124</xmax>
+			<ymax>139</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>413</xmin>
+			<ymin>45</ymin>
+			<xmax>474</xmax>
+			<ymax>131</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/174.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/174.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/174.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/174.xml
new file mode 100644
index 0000000000000000000000000000000000000000..c48ea16e82e6e32945184b4a124fd199ffbdcc9e
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/174.xml	
@@ -0,0 +1,38 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>174.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\174.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>511</width>
+		<height>339</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>232</xmin>
+			<ymin>226</ymin>
+			<xmax>308</xmax>
+			<ymax>334</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>481</xmin>
+			<ymin>213</ymin>
+			<xmax>511</xmax>
+			<ymax>306</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/175.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/175.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/175.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/175.xml
new file mode 100644
index 0000000000000000000000000000000000000000..300f112bd22eee858f11f2ce4cd8f4483ae9941d
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/175.xml	
@@ -0,0 +1,38 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>175.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\175.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>338</width>
+		<height>507</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>100</xmin>
+			<ymin>223</ymin>
+			<xmax>263</xmax>
+			<ymax>439</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>51</xmin>
+			<ymin>26</ymin>
+			<xmax>258</xmax>
+			<ymax>188</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/176.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/176.jpg
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Binary files /dev/null and b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/176.jpg differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/176.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/176.xml
new file mode 100644
index 0000000000000000000000000000000000000000..b5ff0834e0b3e5b442d87eae4106a92641ede84b
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/176.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>176.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\176.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>479</width>
+		<height>359</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>83</xmin>
+			<ymin>170</ymin>
+			<xmax>169</xmax>
+			<ymax>302</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/177.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/177.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/177.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/177.xml
new file mode 100644
index 0000000000000000000000000000000000000000..2db0a0aa4944d025381e9c230f64877116ff7e77
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/177.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>177.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\177.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>1024</width>
+		<height>1024</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>360</xmin>
+			<ymin>314</ymin>
+			<xmax>649</xmax>
+			<ymax>734</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/178.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/178.jpg
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Binary files /dev/null and b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/178.jpg differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/178.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/178.xml
new file mode 100644
index 0000000000000000000000000000000000000000..5d23130a2887603ed8eb5caaee9298b92a7dd6f1
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/178.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>178.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\178.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>371</width>
+		<height>464</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>232</xmin>
+			<ymin>235</ymin>
+			<xmax>347</xmax>
+			<ymax>416</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/179.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/179.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/179.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/179.xml
new file mode 100644
index 0000000000000000000000000000000000000000..bd6fa60952d69400e9a9eabbfd9a21411bd15951
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/179.xml	
@@ -0,0 +1,122 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>179.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\179.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>512</width>
+		<height>335</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>198</xmin>
+			<ymin>204</ymin>
+			<xmax>279</xmax>
+			<ymax>333</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>356</xmin>
+			<ymin>161</ymin>
+			<xmax>432</xmax>
+			<ymax>279</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>397</xmin>
+			<ymin>109</ymin>
+			<xmax>455</xmax>
+			<ymax>211</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>375</xmin>
+			<ymin>48</ymin>
+			<xmax>420</xmax>
+			<ymax>125</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>250</xmin>
+			<ymin>8</ymin>
+			<xmax>290</xmax>
+			<ymax>69</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>325</xmin>
+			<ymin>58</ymin>
+			<xmax>375</xmax>
+			<ymax>100</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>145</xmin>
+			<ymin>29</ymin>
+			<xmax>197</xmax>
+			<ymax>97</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>43</xmin>
+			<ymin>83</ymin>
+			<xmax>103</xmax>
+			<ymax>177</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>91</xmin>
+			<ymin>202</ymin>
+			<xmax>165</xmax>
+			<ymax>286</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/18.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/18.jpg
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Binary files /dev/null and b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/18.jpg differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/18.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/18.xml
new file mode 100644
index 0000000000000000000000000000000000000000..e68fd03950d2b0ce96d1a6c738dfa1b43c73d52e
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/18.xml	
@@ -0,0 +1,50 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>18.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\18.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>338</width>
+		<height>507</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>1</xmin>
+			<ymin>82</ymin>
+			<xmax>164</xmax>
+			<ymax>499</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>39</xmin>
+			<ymin>1</ymin>
+			<xmax>169</xmax>
+			<ymax>221</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>148</xmin>
+			<ymin>1</ymin>
+			<xmax>227</xmax>
+			<ymax>128</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/180.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/180.jpg
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Binary files /dev/null and b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/180.jpg differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/180.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/180.xml
new file mode 100644
index 0000000000000000000000000000000000000000..345f2e21caf998cfc9fe9f7767720238915be188
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/180.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>180.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\180.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>326</width>
+		<height>527</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>27</xmin>
+			<ymin>209</ymin>
+			<xmax>154</xmax>
+			<ymax>481</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/181.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/181.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/181.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/181.xml
new file mode 100644
index 0000000000000000000000000000000000000000..1cb0ae43293c25570965af86ba919c3e2aaa893b
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/181.xml	
@@ -0,0 +1,38 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>181.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\181.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>359</width>
+		<height>479</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>156</xmin>
+			<ymin>366</ymin>
+			<xmax>201</xmax>
+			<ymax>463</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>265</xmin>
+			<ymin>372</ymin>
+			<xmax>306</xmax>
+			<ymax>467</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/182.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/182.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/182.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/182.xml
new file mode 100644
index 0000000000000000000000000000000000000000..3da8b19d8b5d939ef76cabf3acf8c98c3611427d
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/182.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>182.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\182.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>359</width>
+		<height>479</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>175</xmin>
+			<ymin>42</ymin>
+			<xmax>292</xmax>
+			<ymax>277</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/183.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/183.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/183.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/183.xml
new file mode 100644
index 0000000000000000000000000000000000000000..d2e7a0d193db3e42bcc0f507bccac9e3d3c25f5c
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/183.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>183.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\183.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>344</width>
+		<height>502</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>130</xmin>
+			<ymin>151</ymin>
+			<xmax>282</xmax>
+			<ymax>426</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/184.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/184.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/184.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/184.xml
new file mode 100644
index 0000000000000000000000000000000000000000..12c9406e42884d12edac7ee08ae37054a8f4d8d5
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/184.xml	
@@ -0,0 +1,50 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>184.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\184.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>338</width>
+		<height>507</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>1</xmin>
+			<ymin>82</ymin>
+			<xmax>165</xmax>
+			<ymax>502</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>82</xmin>
+			<ymin>1</ymin>
+			<xmax>165</xmax>
+			<ymax>219</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>151</xmin>
+			<ymin>1</ymin>
+			<xmax>225</xmax>
+			<ymax>129</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/185.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/185.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/185.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/185.xml
new file mode 100644
index 0000000000000000000000000000000000000000..2ae38611e918f26c577d6d5a55ec633ea7e49f96
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/185.xml	
@@ -0,0 +1,50 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>185.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\185.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>506</width>
+		<height>338</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>251</xmin>
+			<ymin>152</ymin>
+			<xmax>302</xmax>
+			<ymax>235</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>246</xmin>
+			<ymin>149</ymin>
+			<xmax>272</xmax>
+			<ymax>209</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>242</xmin>
+			<ymin>145</ymin>
+			<xmax>258</xmax>
+			<ymax>184</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/186.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/186.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/186.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/186.xml
new file mode 100644
index 0000000000000000000000000000000000000000..1b9caedcfd4b05dd79f03205add860bcbbbb4794
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/186.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>186.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\186.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>338</width>
+		<height>507</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>154</xmin>
+			<ymin>205</ymin>
+			<xmax>284</xmax>
+			<ymax>423</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/187.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/187.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/187.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/187.xml
new file mode 100644
index 0000000000000000000000000000000000000000..41fac624c56a6863c634e82e4b65467988bad2fb
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/187.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>187.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\187.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>338</width>
+		<height>507</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>96</xmin>
+			<ymin>64</ymin>
+			<xmax>232</xmax>
+			<ymax>283</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/188.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/188.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/188.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/188.xml
new file mode 100644
index 0000000000000000000000000000000000000000..86fe7763385afe643821471b39f8de442412e285
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/188.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>188.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\188.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>350</width>
+		<height>490</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>60</xmin>
+			<ymin>36</ymin>
+			<xmax>291</xmax>
+			<ymax>457</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/189.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/189.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/189.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/189.xml
new file mode 100644
index 0000000000000000000000000000000000000000..331abb6107d0602e667d296443c3b6607dcd758e
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/189.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>189.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\189.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>509</width>
+		<height>336</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>351</xmin>
+			<ymin>214</ymin>
+			<xmax>417</xmax>
+			<ymax>300</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/19.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/19.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/19.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/19.xml
new file mode 100644
index 0000000000000000000000000000000000000000..9a03aee7b077abf3840bfca95d273e625ff36863
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/19.xml	
@@ -0,0 +1,62 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>19.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\19.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>507</width>
+		<height>338</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>73</xmin>
+			<ymin>48</ymin>
+			<xmax>258</xmax>
+			<ymax>314</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>168</xmin>
+			<ymin>59</ymin>
+			<xmax>232</xmax>
+			<ymax>189</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>201</xmin>
+			<ymin>61</ymin>
+			<xmax>251</xmax>
+			<ymax>147</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>224</xmin>
+			<ymin>63</ymin>
+			<xmax>255</xmax>
+			<ymax>116</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/190.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/190.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/190.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/190.xml
new file mode 100644
index 0000000000000000000000000000000000000000..cfb3760d0a0fce8a8be2782a0a593eef288e78f0
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/190.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>190.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\190.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>478</width>
+		<height>359</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>163</xmin>
+			<ymin>64</ymin>
+			<xmax>294</xmax>
+			<ymax>269</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/191.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/191.jpg
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/191.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>191.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\191.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>478</width>
+		<height>359</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>255</xmin>
+			<ymin>205</ymin>
+			<xmax>314</xmax>
+			<ymax>330</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/192.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/192.jpg
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/192.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>192.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\192.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>359</width>
+		<height>478</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>74</xmin>
+			<ymin>76</ymin>
+			<xmax>171</xmax>
+			<ymax>260</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/193.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/193.jpg
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/193.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>193.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\193.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>338</width>
+		<height>507</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>116</xmin>
+			<ymin>52</ymin>
+			<xmax>210</xmax>
+			<ymax>276</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/194.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/194.jpg
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/194.xml	
@@ -0,0 +1,50 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>194.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\194.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>359</width>
+		<height>479</height>
+		<depth>1</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>165</xmin>
+			<ymin>28</ymin>
+			<xmax>243</xmax>
+			<ymax>158</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>267</xmin>
+			<ymin>150</ymin>
+			<xmax>359</xmax>
+			<ymax>430</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>151</xmin>
+			<ymin>1</ymin>
+			<xmax>189</xmax>
+			<ymax>85</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/195.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/195.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/195.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/195.xml
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/195.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>195.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\195.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>507</width>
+		<height>338</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>133</xmin>
+			<ymin>102</ymin>
+			<xmax>235</xmax>
+			<ymax>257</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/196.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/196.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/196.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/196.xml
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/196.xml	
@@ -0,0 +1,38 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>196.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\196.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>507</width>
+		<height>338</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>74</xmin>
+			<ymin>115</ymin>
+			<xmax>177</xmax>
+			<ymax>315</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>213</xmin>
+			<ymin>130</ymin>
+			<xmax>256</xmax>
+			<ymax>196</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/197.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/197.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/197.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/197.xml
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/197.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>197.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\197.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>338</width>
+		<height>507</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>184</xmin>
+			<ymin>63</ymin>
+			<xmax>289</xmax>
+			<ymax>256</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/198.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/198.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/198.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/198.xml
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/198.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>198.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\198.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>359</width>
+		<height>479</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>180</xmin>
+			<ymin>306</ymin>
+			<xmax>244</xmax>
+			<ymax>412</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/199.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/199.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/199.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/199.xml
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/199.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>199.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\199.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>347</width>
+		<height>491</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>45</xmin>
+			<ymin>128</ymin>
+			<xmax>240</xmax>
+			<ymax>453</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/2.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/2.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/2.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/2.xml
new file mode 100644
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/2.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>2.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\2.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>397</width>
+		<height>432</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>63</xmin>
+			<ymin>36</ymin>
+			<xmax>341</xmax>
+			<ymax>374</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/20.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/20.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/20.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/20.xml
new file mode 100644
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/20.xml	
@@ -0,0 +1,38 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>20.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\20.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>338</width>
+		<height>507</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>103</xmin>
+			<ymin>222</ymin>
+			<xmax>266</xmax>
+			<ymax>432</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>55</xmin>
+			<ymin>27</ymin>
+			<xmax>259</xmax>
+			<ymax>191</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/200.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/200.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/200.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/200.xml
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/200.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>200.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\200.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>359</width>
+		<height>479</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>123</xmin>
+			<ymin>257</ymin>
+			<xmax>226</xmax>
+			<ymax>399</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/201.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/201.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/201.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/201.xml
new file mode 100644
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/201.xml	
@@ -0,0 +1,86 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>201.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\201.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>507</width>
+		<height>337</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>360</xmin>
+			<ymin>41</ymin>
+			<xmax>411</xmax>
+			<ymax>120</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>473</xmin>
+			<ymin>32</ymin>
+			<xmax>507</xmax>
+			<ymax>119</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>306</xmin>
+			<ymin>37</ymin>
+			<xmax>350</xmax>
+			<ymax>107</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>318</xmin>
+			<ymin>2</ymin>
+			<xmax>356</xmax>
+			<ymax>59</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>401</xmin>
+			<ymin>1</ymin>
+			<xmax>436</xmax>
+			<ymax>27</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>296</xmin>
+			<ymin>22</ymin>
+			<xmax>312</xmax>
+			<ymax>88</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/202.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/202.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/202.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/202.xml
new file mode 100644
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/202.xml	
@@ -0,0 +1,38 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>202.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\202.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>509</width>
+		<height>339</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>259</xmin>
+			<ymin>117</ymin>
+			<xmax>388</xmax>
+			<ymax>338</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>365</xmin>
+			<ymin>149</ymin>
+			<xmax>509</xmax>
+			<ymax>339</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/203.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/203.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/203.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/203.xml
new file mode 100644
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/203.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>203.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\203.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>553</width>
+		<height>312</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>306</xmin>
+			<ymin>52</ymin>
+			<xmax>449</xmax>
+			<ymax>189</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/204.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/204.jpg
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new file mode 100644
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/204.xml	
@@ -0,0 +1,266 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>204.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\204.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>509</width>
+		<height>339</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>171</xmin>
+			<ymin>267</ymin>
+			<xmax>185</xmax>
+			<ymax>295</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>337</xmin>
+			<ymin>279</ymin>
+			<xmax>360</xmax>
+			<ymax>322</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>308</xmin>
+			<ymin>264</ymin>
+			<xmax>326</xmax>
+			<ymax>292</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>127</xmin>
+			<ymin>281</ymin>
+			<xmax>150</xmax>
+			<ymax>323</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>105</xmin>
+			<ymin>280</ymin>
+			<xmax>125</xmax>
+			<ymax>321</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>461</xmin>
+			<ymin>260</ymin>
+			<xmax>479</xmax>
+			<ymax>288</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>433</xmin>
+			<ymin>254</ymin>
+			<xmax>449</xmax>
+			<ymax>280</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>412</xmin>
+			<ymin>253</ymin>
+			<xmax>426</xmax>
+			<ymax>275</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>395</xmin>
+			<ymin>250</ymin>
+			<xmax>407</xmax>
+			<ymax>271</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>371</xmin>
+			<ymin>247</ymin>
+			<xmax>381</xmax>
+			<ymax>265</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>494</xmin>
+			<ymin>250</ymin>
+			<xmax>507</xmax>
+			<ymax>270</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>456</xmin>
+			<ymin>291</ymin>
+			<xmax>493</xmax>
+			<ymax>339</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>318</xmin>
+			<ymin>298</ymin>
+			<xmax>337</xmax>
+			<ymax>339</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>170</xmin>
+			<ymin>296</ymin>
+			<xmax>190</xmax>
+			<ymax>339</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>194</xmin>
+			<ymin>259</ymin>
+			<xmax>205</xmax>
+			<ymax>281</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>363</xmin>
+			<ymin>244</ymin>
+			<xmax>369</xmax>
+			<ymax>262</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>295</xmin>
+			<ymin>254</ymin>
+			<xmax>302</xmax>
+			<ymax>273</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>151</xmin>
+			<ymin>272</ymin>
+			<xmax>169</xmax>
+			<ymax>306</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>20</xmin>
+			<ymin>301</ymin>
+			<xmax>43</xmax>
+			<ymax>339</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>62</xmin>
+			<ymin>290</ymin>
+			<xmax>84</xmax>
+			<ymax>339</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>365</xmin>
+			<ymin>297</ymin>
+			<xmax>387</xmax>
+			<ymax>339</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/205.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/205.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/205.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/205.xml
new file mode 100644
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/205.xml	
@@ -0,0 +1,134 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>205.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\205.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>507</width>
+		<height>338</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>10</xmin>
+			<ymin>234</ymin>
+			<xmax>45</xmax>
+			<ymax>272</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>63</xmin>
+			<ymin>234</ymin>
+			<xmax>95</xmax>
+			<ymax>271</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>114</xmin>
+			<ymin>234</ymin>
+			<xmax>146</xmax>
+			<ymax>269</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>165</xmin>
+			<ymin>234</ymin>
+			<xmax>195</xmax>
+			<ymax>268</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>216</xmin>
+			<ymin>231</ymin>
+			<xmax>245</xmax>
+			<ymax>267</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>264</xmin>
+			<ymin>233</ymin>
+			<xmax>296</xmax>
+			<ymax>268</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>316</xmin>
+			<ymin>232</ymin>
+			<xmax>347</xmax>
+			<ymax>268</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>367</xmin>
+			<ymin>230</ymin>
+			<xmax>399</xmax>
+			<ymax>268</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>419</xmin>
+			<ymin>230</ymin>
+			<xmax>451</xmax>
+			<ymax>265</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>469</xmin>
+			<ymin>231</ymin>
+			<xmax>504</xmax>
+			<ymax>265</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/206.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/206.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/206.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/206.xml
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/206.xml	
@@ -0,0 +1,38 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>206.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\206.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>338</width>
+		<height>507</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>104</xmin>
+			<ymin>218</ymin>
+			<xmax>263</xmax>
+			<ymax>435</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>54</xmin>
+			<ymin>30</ymin>
+			<xmax>259</xmax>
+			<ymax>190</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/207.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/207.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/207.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/207.xml
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/207.xml	
@@ -0,0 +1,62 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>207.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\207.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>510</width>
+		<height>339</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>252</xmin>
+			<ymin>195</ymin>
+			<xmax>299</xmax>
+			<ymax>277</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>387</xmin>
+			<ymin>186</ymin>
+			<xmax>433</xmax>
+			<ymax>260</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>449</xmin>
+			<ymin>183</ymin>
+			<xmax>492</xmax>
+			<ymax>250</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>227</xmin>
+			<ymin>218</ymin>
+			<xmax>247</xmax>
+			<ymax>271</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/208.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/208.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/208.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/208.xml
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/208.xml	
@@ -0,0 +1,74 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>208.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\208.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>507</width>
+		<height>338</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>20</xmin>
+			<ymin>157</ymin>
+			<xmax>92</xmax>
+			<ymax>246</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>188</xmin>
+			<ymin>97</ymin>
+			<xmax>242</xmax>
+			<ymax>161</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>314</xmin>
+			<ymin>56</ymin>
+			<xmax>355</xmax>
+			<ymax>108</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>406</xmin>
+			<ymin>29</ymin>
+			<xmax>441</xmax>
+			<ymax>73</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>465</xmin>
+			<ymin>8</ymin>
+			<xmax>494</xmax>
+			<ymax>45</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/209.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/209.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/209.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/209.xml
new file mode 100644
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/209.xml	
@@ -0,0 +1,74 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>209.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\209.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>341</width>
+		<height>502</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>1</xmin>
+			<ymin>245</ymin>
+			<xmax>124</xmax>
+			<ymax>430</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>115</xmin>
+			<ymin>102</ymin>
+			<xmax>210</xmax>
+			<ymax>203</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>175</xmin>
+			<ymin>41</ymin>
+			<xmax>232</xmax>
+			<ymax>110</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>231</xmin>
+			<ymin>15</ymin>
+			<xmax>278</xmax>
+			<ymax>74</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>267</xmin>
+			<ymin>1</ymin>
+			<xmax>309</xmax>
+			<ymax>39</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/21.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/21.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/21.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/21.xml
new file mode 100644
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/21.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>21.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\21.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>337</width>
+		<height>508</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>14</xmin>
+			<ymin>9</ymin>
+			<xmax>320</xmax>
+			<ymax>477</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/210.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/210.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/210.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/210.xml
new file mode 100644
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/210.xml	
@@ -0,0 +1,50 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>210.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\210.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>359</width>
+		<height>479</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>109</xmin>
+			<ymin>68</ymin>
+			<xmax>169</xmax>
+			<ymax>174</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>215</xmin>
+			<ymin>181</ymin>
+			<xmax>340</xmax>
+			<ymax>302</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>135</xmin>
+			<ymin>141</ymin>
+			<xmax>224</xmax>
+			<ymax>244</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/211.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/211.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/211.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/211.xml
new file mode 100644
index 0000000000000000000000000000000000000000..a68e768a75174393828432202b8f90095f5ef2e1
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/211.xml	
@@ -0,0 +1,86 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>211.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\211.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>507</width>
+		<height>338</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>169</xmin>
+			<ymin>54</ymin>
+			<xmax>228</xmax>
+			<ymax>131</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>109</xmin>
+			<ymin>51</ymin>
+			<xmax>172</xmax>
+			<ymax>134</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>26</xmin>
+			<ymin>52</ymin>
+			<xmax>91</xmax>
+			<ymax>131</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>280</xmin>
+			<ymin>51</ymin>
+			<xmax>334</xmax>
+			<ymax>127</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>351</xmin>
+			<ymin>52</ymin>
+			<xmax>398</xmax>
+			<ymax>126</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>404</xmin>
+			<ymin>50</ymin>
+			<xmax>452</xmax>
+			<ymax>128</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/212.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/212.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/212.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/212.xml
new file mode 100644
index 0000000000000000000000000000000000000000..0fd6509eec8077a0567167eab9795c40e310e932
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/212.xml	
@@ -0,0 +1,98 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>212.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\212.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>507</width>
+		<height>338</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>49</xmin>
+			<ymin>218</ymin>
+			<xmax>81</xmax>
+			<ymax>262</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>146</xmin>
+			<ymin>218</ymin>
+			<xmax>174</xmax>
+			<ymax>260</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>182</xmin>
+			<ymin>218</ymin>
+			<xmax>210</xmax>
+			<ymax>259</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>210</xmin>
+			<ymin>220</ymin>
+			<xmax>230</xmax>
+			<ymax>257</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>230</xmin>
+			<ymin>218</ymin>
+			<xmax>253</xmax>
+			<ymax>256</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>301</xmin>
+			<ymin>218</ymin>
+			<xmax>326</xmax>
+			<ymax>255</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>443</xmin>
+			<ymin>217</ymin>
+			<xmax>474</xmax>
+			<ymax>250</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/213.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/213.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/213.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/213.xml
new file mode 100644
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/213.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>213.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\213.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>509</width>
+		<height>338</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>209</xmin>
+			<ymin>97</ymin>
+			<xmax>319</xmax>
+			<ymax>304</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/214.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/214.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/214.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/214.xml
new file mode 100644
index 0000000000000000000000000000000000000000..b866d7032fc7e4b6c4caba3501ad1c6a49a0552f
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/214.xml	
@@ -0,0 +1,62 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>214.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\214.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>339</width>
+		<height>508</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>52</xmin>
+			<ymin>388</ymin>
+			<xmax>93</xmax>
+			<ymax>495</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>102</xmin>
+			<ymin>365</ymin>
+			<xmax>181</xmax>
+			<ymax>496</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>191</xmin>
+			<ymin>370</ymin>
+			<xmax>248</xmax>
+			<ymax>498</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>240</xmin>
+			<ymin>381</ymin>
+			<xmax>307</xmax>
+			<ymax>501</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/215.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/215.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/215.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/215.xml
new file mode 100644
index 0000000000000000000000000000000000000000..913b736b02989a9b28e2b72ed9a7f4ca7db9841a
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/215.xml	
@@ -0,0 +1,50 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>215.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\215.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>416</width>
+		<height>416</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>104</xmin>
+			<ymin>319</ymin>
+			<xmax>141</xmax>
+			<ymax>380</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>210</xmin>
+			<ymin>321</ymin>
+			<xmax>246</xmax>
+			<ymax>382</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>294</xmin>
+			<ymin>323</ymin>
+			<xmax>329</xmax>
+			<ymax>381</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/216.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/216.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/216.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/216.xml
new file mode 100644
index 0000000000000000000000000000000000000000..68cf18f7ed0afd5dee47d91b1741bc995d7af7f1
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/216.xml	
@@ -0,0 +1,50 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>216.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\216.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>509</width>
+		<height>338</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>143</xmin>
+			<ymin>114</ymin>
+			<xmax>234</xmax>
+			<ymax>273</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>217</xmin>
+			<ymin>152</ymin>
+			<xmax>269</xmax>
+			<ymax>248</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>1</xmin>
+			<ymin>1</ymin>
+			<xmax>114</xmax>
+			<ymax>336</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/217.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/217.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/217.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/217.xml
new file mode 100644
index 0000000000000000000000000000000000000000..ee07ea4820f118bb83dc62952249cdc8493c8504
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/217.xml	
@@ -0,0 +1,50 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>217.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\217.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>341</width>
+		<height>506</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>1</xmin>
+			<ymin>219</ymin>
+			<xmax>142</xmax>
+			<ymax>500</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>238</xmin>
+			<ymin>240</ymin>
+			<xmax>341</xmax>
+			<ymax>439</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>304</xmin>
+			<ymin>283</ymin>
+			<xmax>341</xmax>
+			<ymax>391</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/218.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/218.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/218.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/218.xml
new file mode 100644
index 0000000000000000000000000000000000000000..0797498d60e4d2aa78900300a939aa3391aa7907
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/218.xml	
@@ -0,0 +1,86 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>218.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\218.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>508</width>
+		<height>338</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>1</xmin>
+			<ymin>34</ymin>
+			<xmax>132</xmax>
+			<ymax>338</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>140</xmin>
+			<ymin>24</ymin>
+			<xmax>246</xmax>
+			<ymax>262</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>244</xmin>
+			<ymin>22</ymin>
+			<xmax>320</xmax>
+			<ymax>214</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>326</xmin>
+			<ymin>15</ymin>
+			<xmax>385</xmax>
+			<ymax>153</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>392</xmin>
+			<ymin>4</ymin>
+			<xmax>420</xmax>
+			<ymax>97</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>376</xmin>
+			<ymin>7</ymin>
+			<xmax>394</xmax>
+			<ymax>126</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/219.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/219.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/219.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/219.xml
new file mode 100644
index 0000000000000000000000000000000000000000..9dccd54a75bf67c6e31c2adac5c7fe30f836c03b
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/219.xml	
@@ -0,0 +1,194 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>219.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\219.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>510</width>
+		<height>337</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>308</xmin>
+			<ymin>168</ymin>
+			<xmax>402</xmax>
+			<ymax>327</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>297</xmin>
+			<ymin>120</ymin>
+			<xmax>360</xmax>
+			<ymax>250</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>279</xmin>
+			<ymin>92</ymin>
+			<xmax>326</xmax>
+			<ymax>207</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>369</xmin>
+			<ymin>24</ymin>
+			<xmax>398</xmax>
+			<ymax>77</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>432</xmin>
+			<ymin>20</ymin>
+			<xmax>459</xmax>
+			<ymax>75</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>313</xmin>
+			<ymin>21</ymin>
+			<xmax>341</xmax>
+			<ymax>76</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>152</xmin>
+			<ymin>16</ymin>
+			<xmax>184</xmax>
+			<ymax>64</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>121</xmin>
+			<ymin>15</ymin>
+			<xmax>153</xmax>
+			<ymax>61</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>103</xmin>
+			<ymin>15</ymin>
+			<xmax>121</xmax>
+			<ymax>65</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>72</xmin>
+			<ymin>16</ymin>
+			<xmax>102</xmax>
+			<ymax>62</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>39</xmin>
+			<ymin>17</ymin>
+			<xmax>75</xmax>
+			<ymax>67</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>15</xmin>
+			<ymin>17</ymin>
+			<xmax>51</xmax>
+			<ymax>69</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>260</xmin>
+			<ymin>74</ymin>
+			<xmax>311</xmax>
+			<ymax>173</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>252</xmin>
+			<ymin>61</ymin>
+			<xmax>291</xmax>
+			<ymax>148</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>223</xmin>
+			<ymin>30</ymin>
+			<xmax>244</xmax>
+			<ymax>87</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/22.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/22.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/22.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/22.xml
new file mode 100644
index 0000000000000000000000000000000000000000..88bd6c6ca793f1ee76450359ecef7778a8253a82
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/22.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>22.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\22.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>337</width>
+		<height>508</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>3</xmin>
+			<ymin>37</ymin>
+			<xmax>333</xmax>
+			<ymax>478</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/220.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/220.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/220.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/220.xml
new file mode 100644
index 0000000000000000000000000000000000000000..31f460ffd7ee58c79ee058a83f5a724d53a0c34f
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/220.xml	
@@ -0,0 +1,74 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>220.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\220.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>505</width>
+		<height>342</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>202</xmin>
+			<ymin>92</ymin>
+			<xmax>249</xmax>
+			<ymax>178</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>225</xmin>
+			<ymin>182</ymin>
+			<xmax>419</xmax>
+			<ymax>321</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>323</xmin>
+			<ymin>111</ymin>
+			<xmax>370</xmax>
+			<ymax>167</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>265</xmin>
+			<ymin>94</ymin>
+			<xmax>286</xmax>
+			<ymax>138</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>230</xmin>
+			<ymin>109</ymin>
+			<xmax>247</xmax>
+			<ymax>134</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/221.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/221.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/221.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/221.xml
new file mode 100644
index 0000000000000000000000000000000000000000..30214aa533d6a9481bcc6f7df2d2e6921b844579
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/221.xml	
@@ -0,0 +1,170 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>221.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\221.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>507</width>
+		<height>338</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>90</xmin>
+			<ymin>196</ymin>
+			<xmax>118</xmax>
+			<ymax>241</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>118</xmin>
+			<ymin>197</ymin>
+			<xmax>147</xmax>
+			<ymax>241</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>145</xmin>
+			<ymin>197</ymin>
+			<xmax>174</xmax>
+			<ymax>243</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>174</xmin>
+			<ymin>197</ymin>
+			<xmax>201</xmax>
+			<ymax>243</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>200</xmin>
+			<ymin>199</ymin>
+			<xmax>227</xmax>
+			<ymax>244</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>227</xmin>
+			<ymin>198</ymin>
+			<xmax>254</xmax>
+			<ymax>242</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>255</xmin>
+			<ymin>197</ymin>
+			<xmax>282</xmax>
+			<ymax>243</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>282</xmin>
+			<ymin>198</ymin>
+			<xmax>308</xmax>
+			<ymax>243</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>309</xmin>
+			<ymin>198</ymin>
+			<xmax>337</xmax>
+			<ymax>244</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>338</xmin>
+			<ymin>196</ymin>
+			<xmax>362</xmax>
+			<ymax>247</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>362</xmin>
+			<ymin>197</ymin>
+			<xmax>391</xmax>
+			<ymax>246</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>392</xmin>
+			<ymin>198</ymin>
+			<xmax>417</xmax>
+			<ymax>244</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>417</xmin>
+			<ymin>195</ymin>
+			<xmax>445</xmax>
+			<ymax>244</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/222.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/222.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/222.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/222.xml
new file mode 100644
index 0000000000000000000000000000000000000000..0d82b5c3ed1365cd03f0ee7e759f9710b873d871
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/222.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>222.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\222.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>339</width>
+		<height>509</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>1</xmin>
+			<ymin>140</ymin>
+			<xmax>179</xmax>
+			<ymax>497</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/223.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/223.jpg
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Binary files /dev/null and b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/223.jpg differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/223.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/223.xml
new file mode 100644
index 0000000000000000000000000000000000000000..25a28926da320788a8e1e7645fa5080192ec6276
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/223.xml	
@@ -0,0 +1,38 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>223.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\223.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>507</width>
+		<height>338</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>99</xmin>
+			<ymin>60</ymin>
+			<xmax>194</xmax>
+			<ymax>234</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>279</xmin>
+			<ymin>51</ymin>
+			<xmax>398</xmax>
+			<ymax>303</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/224.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/224.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/224.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/224.xml
new file mode 100644
index 0000000000000000000000000000000000000000..2b117f04131ab5d1bb6a790b3a85f1977c305ef4
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/224.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>224.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\224.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>338</width>
+		<height>507</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>135</xmin>
+			<ymin>182</ymin>
+			<xmax>266</xmax>
+			<ymax>462</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/225.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/225.jpg
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Binary files /dev/null and b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/225.jpg differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/225.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/225.xml
new file mode 100644
index 0000000000000000000000000000000000000000..57289b095fc96c0af8251229bd3fdaa9355cb484
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/225.xml	
@@ -0,0 +1,38 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>225.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\225.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>479</width>
+		<height>359</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>180</xmin>
+			<ymin>139</ymin>
+			<xmax>236</xmax>
+			<ymax>234</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>252</xmin>
+			<ymin>126</ymin>
+			<xmax>304</xmax>
+			<ymax>213</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/226.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/226.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/226.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/226.xml
new file mode 100644
index 0000000000000000000000000000000000000000..7b7abaa075930e80a3de28d2ee554890feeabf1a
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/226.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>226.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\226.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>678</width>
+		<height>1024</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>123</xmin>
+			<ymin>145</ymin>
+			<xmax>573</xmax>
+			<ymax>970</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/227.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/227.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/227.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/227.xml
new file mode 100644
index 0000000000000000000000000000000000000000..49b939d640ee4152d439b8620ce3deefac6bbd0a
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/227.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>227.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\227.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>338</width>
+		<height>511</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>16</xmin>
+			<ymin>18</ymin>
+			<xmax>280</xmax>
+			<ymax>499</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/228.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/228.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/228.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/228.xml
new file mode 100644
index 0000000000000000000000000000000000000000..9adab7e03408dc312f79055fca43f9a67b25115b
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/228.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>228.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\228.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>170</width>
+		<height>170</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>85</xmin>
+			<ymin>16</ymin>
+			<xmax>159</xmax>
+			<ymax>147</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/229.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/229.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/229.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/229.xml
new file mode 100644
index 0000000000000000000000000000000000000000..944b54f47c74890477224450f1aff7127ca3b421
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/229.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>229.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\229.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>416</width>
+		<height>416</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>214</xmin>
+			<ymin>38</ymin>
+			<xmax>387</xmax>
+			<ymax>363</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/23.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/23.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/23.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/23.xml
new file mode 100644
index 0000000000000000000000000000000000000000..85ba55bbaf08d5df33644dc84a711c8e5b1b2a07
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/23.xml	
@@ -0,0 +1,38 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>23.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\23.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>478</width>
+		<height>359</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>16</xmin>
+			<ymin>29</ymin>
+			<xmax>261</xmax>
+			<ymax>310</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>242</xmin>
+			<ymin>76</ymin>
+			<xmax>439</xmax>
+			<ymax>271</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/230.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/230.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/230.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/230.xml
new file mode 100644
index 0000000000000000000000000000000000000000..d056eb59e926bbd8f90485ee74a71c63d09617d8
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/230.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>230.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\230.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>356</width>
+		<height>481</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>101</xmin>
+			<ymin>88</ymin>
+			<xmax>244</xmax>
+			<ymax>318</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/231.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/231.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/231.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/231.xml
new file mode 100644
index 0000000000000000000000000000000000000000..d3a69a02992ba4901e78e22665506209ddaad6bc
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/231.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>231.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\231.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>338</width>
+		<height>511</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>97</xmin>
+			<ymin>87</ymin>
+			<xmax>234</xmax>
+			<ymax>335</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/232.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/232.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/232.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/232.xml
new file mode 100644
index 0000000000000000000000000000000000000000..06d42a1305233062b9f57e54278c63873a1e555b
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/232.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>232.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\232.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>416</width>
+		<height>416</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>24</xmin>
+			<ymin>51</ymin>
+			<xmax>223</xmax>
+			<ymax>387</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/233.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/233.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/233.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/233.xml
new file mode 100644
index 0000000000000000000000000000000000000000..d6c3bc6fee80becebfe589cf671dd066b673aa4f
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/233.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>233.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\233.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>482</width>
+		<height>359</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>375</xmin>
+			<ymin>1</ymin>
+			<xmax>479</xmax>
+			<ymax>210</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/234.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/234.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/234.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/234.xml
new file mode 100644
index 0000000000000000000000000000000000000000..a6a4e2ddc7924bbeacf6ab90ac661a1dbb83e61d
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/234.xml	
@@ -0,0 +1,38 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>234.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\234.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>371</width>
+		<height>464</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>6</xmin>
+			<ymin>155</ymin>
+			<xmax>173</xmax>
+			<ymax>456</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>71</xmin>
+			<ymin>23</ymin>
+			<xmax>224</xmax>
+			<ymax>371</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/235.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/235.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/235.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/235.xml
new file mode 100644
index 0000000000000000000000000000000000000000..39f8da132e732cd8868bfb01c3a222fafeb16cbf
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/235.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>235.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\235.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>509</width>
+		<height>339</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>271</xmin>
+			<ymin>42</ymin>
+			<xmax>386</xmax>
+			<ymax>206</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/236.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/236.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/236.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/236.xml
new file mode 100644
index 0000000000000000000000000000000000000000..26ba8bc91a84826611b19c7018710a94c2127497
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/236.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>236.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\236.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>384</width>
+		<height>450</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>25</xmin>
+			<ymin>80</ymin>
+			<xmax>225</xmax>
+			<ymax>418</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/237.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/237.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/237.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/237.xml
new file mode 100644
index 0000000000000000000000000000000000000000..add17552a010f76d44ca23368e5bf3e25e92b78a
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/237.xml	
@@ -0,0 +1,62 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>237.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\237.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>357</width>
+		<height>479</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>39</xmin>
+			<ymin>317</ymin>
+			<xmax>122</xmax>
+			<ymax>427</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>30</xmin>
+			<ymin>288</ymin>
+			<xmax>72</xmax>
+			<ymax>361</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>17</xmin>
+			<ymin>278</ymin>
+			<xmax>46</xmax>
+			<ymax>332</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>7</xmin>
+			<ymin>266</ymin>
+			<xmax>26</xmax>
+			<ymax>298</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/238.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/238.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/238.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/238.xml
new file mode 100644
index 0000000000000000000000000000000000000000..093e95bfbe254312e2fdbda602fd8b7fbdea7c92
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/238.xml	
@@ -0,0 +1,74 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>238.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\238.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>478</width>
+		<height>359</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>80</xmin>
+			<ymin>35</ymin>
+			<xmax>182</xmax>
+			<ymax>209</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>152</xmin>
+			<ymin>14</ymin>
+			<xmax>238</xmax>
+			<ymax>161</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>154</xmin>
+			<ymin>84</ymin>
+			<xmax>259</xmax>
+			<ymax>281</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>269</xmin>
+			<ymin>72</ymin>
+			<xmax>360</xmax>
+			<ymax>256</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>249</xmin>
+			<ymin>26</ymin>
+			<xmax>320</xmax>
+			<ymax>174</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/239.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/239.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/239.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/239.xml
new file mode 100644
index 0000000000000000000000000000000000000000..c05abbfabfad744e3b77906448753800812b1f86
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/239.xml	
@@ -0,0 +1,38 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>239.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\239.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>509</width>
+		<height>339</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>172</xmin>
+			<ymin>54</ymin>
+			<xmax>225</xmax>
+			<ymax>147</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>340</xmin>
+			<ymin>53</ymin>
+			<xmax>479</xmax>
+			<ymax>326</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/24.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/24.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/24.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/24.xml
new file mode 100644
index 0000000000000000000000000000000000000000..ebbeef64420c6fc0164f424155052c4bf7820636
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/24.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>24.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\24.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>339</width>
+		<height>509</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>34</xmin>
+			<ymin>65</ymin>
+			<xmax>319</xmax>
+			<ymax>474</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/240.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/240.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/240.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/240.xml
new file mode 100644
index 0000000000000000000000000000000000000000..3b707f184b432f9c9f8fbb8030ed52ded558b84c
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/240.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>240.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\240.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>507</width>
+		<height>339</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>117</xmin>
+			<ymin>71</ymin>
+			<xmax>187</xmax>
+			<ymax>185</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/241.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/241.jpg
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Binary files /dev/null and b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/241.jpg differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/241.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/241.xml
new file mode 100644
index 0000000000000000000000000000000000000000..3b3992d4cb4d1bbd644f4999d8f51037c63ba0c5
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/241.xml	
@@ -0,0 +1,38 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>241.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\241.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>509</width>
+		<height>339</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>75</xmin>
+			<ymin>44</ymin>
+			<xmax>138</xmax>
+			<ymax>133</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>279</xmin>
+			<ymin>162</ymin>
+			<xmax>357</xmax>
+			<ymax>282</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/242.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/242.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/242.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/242.xml
new file mode 100644
index 0000000000000000000000000000000000000000..7c184931388544e153ce3c688a22bdc2ad1a65c1
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/242.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>242.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\242.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>576</width>
+		<height>1024</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>207</xmin>
+			<ymin>34</ymin>
+			<xmax>552</xmax>
+			<ymax>668</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/243.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/243.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/243.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/243.xml
new file mode 100644
index 0000000000000000000000000000000000000000..004ad9ae2dab2731f3f5fae99b13b907f109fe05
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/243.xml	
@@ -0,0 +1,74 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>243.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\243.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>507</width>
+		<height>338</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>209</xmin>
+			<ymin>30</ymin>
+			<xmax>315</xmax>
+			<ymax>226</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>25</xmin>
+			<ymin>1</ymin>
+			<xmax>113</xmax>
+			<ymax>125</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>83</xmin>
+			<ymin>1</ymin>
+			<xmax>163</xmax>
+			<ymax>81</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>262</xmin>
+			<ymin>114</ymin>
+			<xmax>410</xmax>
+			<ymax>338</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>270</xmin>
+			<ymin>1</ymin>
+			<xmax>332</xmax>
+			<ymax>124</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/244.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/244.jpg
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Binary files /dev/null and b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/244.jpg differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/244.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/244.xml
new file mode 100644
index 0000000000000000000000000000000000000000..cc9009d4d839ea3e4a11cd9f476f2560285cd103
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/244.xml	
@@ -0,0 +1,122 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>244.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\244.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>338</width>
+		<height>507</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>272</xmin>
+			<ymin>163</ymin>
+			<xmax>338</xmax>
+			<ymax>272</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>212</xmin>
+			<ymin>175</ymin>
+			<xmax>271</xmax>
+			<ymax>264</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>169</xmin>
+			<ymin>181</ymin>
+			<xmax>223</xmax>
+			<ymax>259</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>137</xmin>
+			<ymin>189</ymin>
+			<xmax>183</xmax>
+			<ymax>258</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>115</xmin>
+			<ymin>194</ymin>
+			<xmax>145</xmax>
+			<ymax>256</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>94</xmin>
+			<ymin>198</ymin>
+			<xmax>124</xmax>
+			<ymax>253</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>80</xmin>
+			<ymin>200</ymin>
+			<xmax>104</xmax>
+			<ymax>252</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>68</xmin>
+			<ymin>203</ymin>
+			<xmax>89</xmax>
+			<ymax>250</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>58</xmin>
+			<ymin>206</ymin>
+			<xmax>73</xmax>
+			<ymax>247</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/245.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/245.jpg
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Binary files /dev/null and b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/245.jpg differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/245.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/245.xml
new file mode 100644
index 0000000000000000000000000000000000000000..1ea01ddec8debf2d735a3f18ea90008c9750d161
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/245.xml	
@@ -0,0 +1,98 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>245.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\245.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>338</width>
+		<height>507</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>1</xmin>
+			<ymin>275</ymin>
+			<xmax>68</xmax>
+			<ymax>362</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>72</xmin>
+			<ymin>286</ymin>
+			<xmax>124</xmax>
+			<ymax>358</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>123</xmin>
+			<ymin>294</ymin>
+			<xmax>164</xmax>
+			<ymax>354</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>159</xmin>
+			<ymin>299</ymin>
+			<xmax>193</xmax>
+			<ymax>354</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>190</xmin>
+			<ymin>302</ymin>
+			<xmax>215</xmax>
+			<ymax>354</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>208</xmin>
+			<ymin>306</ymin>
+			<xmax>232</xmax>
+			<ymax>354</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>227</xmin>
+			<ymin>312</ymin>
+			<xmax>249</xmax>
+			<ymax>350</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/246.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/246.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/246.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/246.xml
new file mode 100644
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/246.xml	
@@ -0,0 +1,86 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>246.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\246.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>338</width>
+		<height>507</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>1</xmin>
+			<ymin>199</ymin>
+			<xmax>111</xmax>
+			<ymax>353</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>87</xmin>
+			<ymin>238</ymin>
+			<xmax>158</xmax>
+			<ymax>346</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>138</xmin>
+			<ymin>252</ymin>
+			<xmax>184</xmax>
+			<ymax>340</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>162</xmin>
+			<ymin>267</ymin>
+			<xmax>204</xmax>
+			<ymax>338</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>187</xmin>
+			<ymin>275</ymin>
+			<xmax>211</xmax>
+			<ymax>337</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>210</xmin>
+			<ymin>287</ymin>
+			<xmax>225</xmax>
+			<ymax>335</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/247.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/247.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/247.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/247.xml
new file mode 100644
index 0000000000000000000000000000000000000000..151d36129f29ad732edc32b3a72394f32b0d8a39
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/247.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>247.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\247.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>509</width>
+		<height>339</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>197</xmin>
+			<ymin>101</ymin>
+			<xmax>344</xmax>
+			<ymax>293</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/248.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/248.jpg
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Binary files /dev/null and b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/248.jpg differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/248.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/248.xml
new file mode 100644
index 0000000000000000000000000000000000000000..8a568d3149339cd72a913968fbd12a3c19f04568
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/248.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>248.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\248.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>509</width>
+		<height>339</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>296</xmin>
+			<ymin>184</ymin>
+			<xmax>408</xmax>
+			<ymax>319</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/249.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/249.jpg
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Binary files /dev/null and b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/249.jpg differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/249.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/249.xml
new file mode 100644
index 0000000000000000000000000000000000000000..a48aa2ca13482356050272461e63b309381ed7a8
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/249.xml	
@@ -0,0 +1,134 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>249.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\249.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>507</width>
+		<height>338</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>130</xmin>
+			<ymin>259</ymin>
+			<xmax>171</xmax>
+			<ymax>321</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>165</xmin>
+			<ymin>232</ymin>
+			<xmax>199</xmax>
+			<ymax>282</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>189</xmin>
+			<ymin>213</ymin>
+			<xmax>216</xmax>
+			<ymax>255</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>206</xmin>
+			<ymin>200</ymin>
+			<xmax>230</xmax>
+			<ymax>237</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>218</xmin>
+			<ymin>190</ymin>
+			<xmax>239</xmax>
+			<ymax>221</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>230</xmin>
+			<ymin>183</ymin>
+			<xmax>246</xmax>
+			<ymax>211</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>237</xmin>
+			<ymin>178</ymin>
+			<xmax>254</xmax>
+			<ymax>201</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>243</xmin>
+			<ymin>172</ymin>
+			<xmax>259</xmax>
+			<ymax>193</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>256</xmin>
+			<ymin>162</ymin>
+			<xmax>266</xmax>
+			<ymax>181</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>85</xmin>
+			<ymin>306</ymin>
+			<xmax>113</xmax>
+			<ymax>338</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/25.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/25.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/25.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/25.xml
new file mode 100644
index 0000000000000000000000000000000000000000..ff99b86d07e94aa8ae0bb50c3c99074a2773dbc1
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/25.xml	
@@ -0,0 +1,38 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>25.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\25.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>1024</width>
+		<height>989</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>106</xmin>
+			<ymin>107</ymin>
+			<xmax>640</xmax>
+			<ymax>913</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>470</xmin>
+			<ymin>281</ymin>
+			<xmax>936</xmax>
+			<ymax>657</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/250.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/250.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/250.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/250.xml
new file mode 100644
index 0000000000000000000000000000000000000000..4db7a2b25d00279a3df16b09a64b8228049699c5
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/250.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>250.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\250.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>533</width>
+		<height>651</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>172</xmin>
+			<ymin>201</ymin>
+			<xmax>457</xmax>
+			<ymax>594</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/251.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/251.jpg
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Binary files /dev/null and b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/251.jpg differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/251.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/251.xml
new file mode 100644
index 0000000000000000000000000000000000000000..7fcc8409c4e90d8ad740eb6ce5fa1cdac781c6a1
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/251.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>251.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\251.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>353</width>
+		<height>485</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>56</xmin>
+			<ymin>3</ymin>
+			<xmax>292</xmax>
+			<ymax>445</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/252.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/252.jpg
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Binary files /dev/null and b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/252.jpg differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/252.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/252.xml
new file mode 100644
index 0000000000000000000000000000000000000000..dadb13d259b16ff2a085bd66396e33e467727e9b
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/252.xml	
@@ -0,0 +1,122 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>252.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\252.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>375</width>
+		<height>458</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>181</xmin>
+			<ymin>298</ymin>
+			<xmax>231</xmax>
+			<ymax>389</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>205</xmin>
+			<ymin>242</ymin>
+			<xmax>243</xmax>
+			<ymax>316</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>247</xmin>
+			<ymin>205</ymin>
+			<xmax>279</xmax>
+			<ymax>266</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>258</xmin>
+			<ymin>172</ymin>
+			<xmax>286</xmax>
+			<ymax>219</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>244</xmin>
+			<ymin>147</ymin>
+			<xmax>265</xmax>
+			<ymax>182</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>198</xmin>
+			<ymin>128</ymin>
+			<xmax>222</xmax>
+			<ymax>158</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>145</xmin>
+			<ymin>129</ymin>
+			<xmax>162</xmax>
+			<ymax>162</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>100</xmin>
+			<ymin>152</ymin>
+			<xmax>123</xmax>
+			<ymax>194</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>84</xmin>
+			<ymin>180</ymin>
+			<xmax>112</xmax>
+			<ymax>234</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/253.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/253.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/253.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/253.xml
new file mode 100644
index 0000000000000000000000000000000000000000..16c933e2bf71d555361d697b4d5ce3cdb81222c8
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/253.xml	
@@ -0,0 +1,38 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>253.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\253.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>338</width>
+		<height>507</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>150</xmin>
+			<ymin>218</ymin>
+			<xmax>262</xmax>
+			<ymax>464</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>273</xmin>
+			<ymin>243</ymin>
+			<xmax>338</xmax>
+			<ymax>507</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/254.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/254.jpg
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Binary files /dev/null and b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/254.jpg differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/254.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/254.xml
new file mode 100644
index 0000000000000000000000000000000000000000..42224d1fb8afba80f3b646ea6bc56f12a356caad
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/254.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>254.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\254.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>478</width>
+		<height>358</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>29</xmin>
+			<ymin>108</ymin>
+			<xmax>423</xmax>
+			<ymax>309</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/255.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/255.jpg
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Binary files /dev/null and b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/255.jpg differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/255.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/255.xml
new file mode 100644
index 0000000000000000000000000000000000000000..e39b53d456ba8157efca896ce47c63537b17c836
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/255.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>255.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\255.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>339</width>
+		<height>506</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>68</xmin>
+			<ymin>52</ymin>
+			<xmax>299</xmax>
+			<ymax>467</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/26.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/26.jpg
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Binary files /dev/null and b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/26.jpg differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/26.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/26.xml
new file mode 100644
index 0000000000000000000000000000000000000000..21e4685b0b0c520c8a9b3c399189003e0b003557
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/26.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>26.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\26.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>686</width>
+		<height>1024</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>120</xmin>
+			<ymin>115</ymin>
+			<xmax>555</xmax>
+			<ymax>963</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/27.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/27.jpg
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Binary files /dev/null and b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/27.jpg differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/27.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/27.xml
new file mode 100644
index 0000000000000000000000000000000000000000..269536d45d00144d322bc0772181ce30b54d0ebc
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/27.xml	
@@ -0,0 +1,86 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>27.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\27.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>602</width>
+		<height>1024</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>177</xmin>
+			<ymin>125</ymin>
+			<xmax>421</xmax>
+			<ymax>697</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>337</xmin>
+			<ymin>269</ymin>
+			<xmax>602</xmax>
+			<ymax>1023</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>168</xmin>
+			<ymin>105</ymin>
+			<xmax>316</xmax>
+			<ymax>464</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>166</xmin>
+			<ymin>115</ymin>
+			<xmax>247</xmax>
+			<ymax>343</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>48</xmin>
+			<ymin>74</ymin>
+			<xmax>124</xmax>
+			<ymax>207</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>1</xmin>
+			<ymin>73</ymin>
+			<xmax>45</xmax>
+			<ymax>179</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/28.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/28.jpg
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Binary files /dev/null and b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/28.jpg differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/28.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/28.xml
new file mode 100644
index 0000000000000000000000000000000000000000..cbd1807eb72ba55ed8fdca85ea4c3ac21ee60edc
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/28.xml	
@@ -0,0 +1,86 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>28.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\28.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>1024</width>
+		<height>658</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>1</xmin>
+			<ymin>46</ymin>
+			<xmax>246</xmax>
+			<ymax>655</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>1</xmin>
+			<ymin>42</ymin>
+			<xmax>265</xmax>
+			<ymax>562</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>431</xmin>
+			<ymin>225</ymin>
+			<xmax>537</xmax>
+			<ymax>393</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>652</xmin>
+			<ymin>244</ymin>
+			<xmax>737</xmax>
+			<ymax>371</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>813</xmin>
+			<ymin>260</ymin>
+			<xmax>894</xmax>
+			<ymax>362</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>316</xmin>
+			<ymin>197</ymin>
+			<xmax>389</xmax>
+			<ymax>432</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/29.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/29.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/29.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/29.xml
new file mode 100644
index 0000000000000000000000000000000000000000..a87ff5cd2153626ebc41d319f3c832a7b08269ee
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/29.xml	
@@ -0,0 +1,74 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>29.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\29.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>1024</width>
+		<height>658</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>390</xmin>
+			<ymin>259</ymin>
+			<xmax>632</xmax>
+			<ymax>417</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>806</xmin>
+			<ymin>256</ymin>
+			<xmax>895</xmax>
+			<ymax>361</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>1</xmin>
+			<ymin>41</ymin>
+			<xmax>261</xmax>
+			<ymax>561</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>250</xmin>
+			<ymin>181</ymin>
+			<xmax>393</xmax>
+			<ymax>430</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>711</xmin>
+			<ymin>251</ymin>
+			<xmax>788</xmax>
+			<ymax>365</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/3.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/3.jpg
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Binary files /dev/null and b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/3.jpg differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/3.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/3.xml
new file mode 100644
index 0000000000000000000000000000000000000000..4c93fbceab56b63b9c500003be55dcd5179ce1ce
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/3.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>3.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\3.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>142</width>
+		<height>170</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>5</xmin>
+			<ymin>10</ymin>
+			<xmax>137</xmax>
+			<ymax>162</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/30.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/30.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/30.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/30.xml
new file mode 100644
index 0000000000000000000000000000000000000000..7ad1e12f8c1589093df8009ac398dcbc0c9eb609
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/30.xml	
@@ -0,0 +1,62 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>30.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\30.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>1024</width>
+		<height>768</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>1</xmin>
+			<ymin>1</ymin>
+			<xmax>297</xmax>
+			<ymax>756</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>48</xmin>
+			<ymin>1</ymin>
+			<xmax>352</xmax>
+			<ymax>523</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>183</xmin>
+			<ymin>48</ymin>
+			<xmax>384</xmax>
+			<ymax>418</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>299</xmin>
+			<ymin>125</ymin>
+			<xmax>589</xmax>
+			<ymax>360</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/31.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/31.jpg
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Binary files /dev/null and b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/31.jpg differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/31.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/31.xml
new file mode 100644
index 0000000000000000000000000000000000000000..95ea8048e389114c90ad1f35dd058f2bd85358e1
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/31.xml	
@@ -0,0 +1,38 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>31.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\31.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>478</width>
+		<height>359</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>19</xmin>
+			<ymin>32</ymin>
+			<xmax>261</xmax>
+			<ymax>309</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>268</xmin>
+			<ymin>76</ymin>
+			<xmax>438</xmax>
+			<ymax>275</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/32.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/32.jpg
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Binary files /dev/null and b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/32.jpg differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/32.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/32.xml
new file mode 100644
index 0000000000000000000000000000000000000000..0f671931ef4e20b4ce89001337764f5eedb11c20
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/32.xml	
@@ -0,0 +1,74 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>32.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\32.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>1024</width>
+		<height>768</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>105</xmin>
+			<ymin>338</ymin>
+			<xmax>289</xmax>
+			<ymax>547</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>229</xmin>
+			<ymin>393</ymin>
+			<xmax>473</xmax>
+			<ymax>594</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>538</xmin>
+			<ymin>395</ymin>
+			<xmax>737</xmax>
+			<ymax>650</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>413</xmin>
+			<ymin>347</ymin>
+			<xmax>566</xmax>
+			<ymax>498</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>650</xmin>
+			<ymin>341</ymin>
+			<xmax>800</xmax>
+			<ymax>466</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/33.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/33.jpg
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Binary files /dev/null and b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/33.jpg differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/33.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/33.xml
new file mode 100644
index 0000000000000000000000000000000000000000..b18fd0d3c5481f088179262b8fbef9fd89a41aec
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/33.xml	
@@ -0,0 +1,62 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>33.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\33.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>507</width>
+		<height>338</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>83</xmin>
+			<ymin>49</ymin>
+			<xmax>258</xmax>
+			<ymax>314</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>181</xmin>
+			<ymin>56</ymin>
+			<xmax>233</xmax>
+			<ymax>191</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>206</xmin>
+			<ymin>60</ymin>
+			<xmax>250</xmax>
+			<ymax>147</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>232</xmin>
+			<ymin>62</ymin>
+			<xmax>255</xmax>
+			<ymax>116</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/34.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/34.jpg
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Binary files /dev/null and b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/34.jpg differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/34.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/34.xml
new file mode 100644
index 0000000000000000000000000000000000000000..4b39b925cc17d9c9d98fcc47f013148a7056bd25
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/34.xml	
@@ -0,0 +1,62 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>34.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\34.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>478</width>
+		<height>359</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>237</xmin>
+			<ymin>23</ymin>
+			<xmax>438</xmax>
+			<ymax>313</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>152</xmin>
+			<ymin>88</ymin>
+			<xmax>233</xmax>
+			<ymax>214</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>110</xmin>
+			<ymin>113</ymin>
+			<xmax>166</xmax>
+			<ymax>189</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>85</xmin>
+			<ymin>150</ymin>
+			<xmax>106</xmax>
+			<ymax>186</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/35.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/35.jpg
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Binary files /dev/null and b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/35.jpg differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/35.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/35.xml
new file mode 100644
index 0000000000000000000000000000000000000000..4b6b98479598bb5468aef6531be82c30aa5f8de2
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/35.xml	
@@ -0,0 +1,86 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>35.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\35.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>507</width>
+		<height>338</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>305</xmin>
+			<ymin>19</ymin>
+			<xmax>480</xmax>
+			<ymax>298</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>150</xmin>
+			<ymin>175</ymin>
+			<xmax>212</xmax>
+			<ymax>262</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>125</xmin>
+			<ymin>198</ymin>
+			<xmax>157</xmax>
+			<ymax>257</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>97</xmin>
+			<ymin>215</ymin>
+			<xmax>120</xmax>
+			<ymax>254</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>86</xmin>
+			<ymin>219</ymin>
+			<xmax>101</xmax>
+			<ymax>254</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>52</xmin>
+			<ymin>229</ymin>
+			<xmax>65</xmax>
+			<ymax>249</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/36.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/36.jpg
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Binary files /dev/null and b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/36.jpg differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/36.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/36.xml
new file mode 100644
index 0000000000000000000000000000000000000000..10a88dcb45c48dfcb952ae030b23363ceabe660f
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/36.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>36.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\36.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>323</width>
+		<height>529</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>1</xmin>
+			<ymin>17</ymin>
+			<xmax>309</xmax>
+			<ymax>504</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/37.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/37.jpg
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Binary files /dev/null and b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/37.jpg differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/37.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/37.xml
new file mode 100644
index 0000000000000000000000000000000000000000..e9741a2467c31304a3fb39feb5e3a6b029004b29
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/37.xml	
@@ -0,0 +1,38 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>37.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\37.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>421</width>
+		<height>407</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>43</xmin>
+			<ymin>39</ymin>
+			<xmax>257</xmax>
+			<ymax>301</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>189</xmin>
+			<ymin>115</ymin>
+			<xmax>384</xmax>
+			<ymax>268</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/38.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/38.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/38.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/38.xml
new file mode 100644
index 0000000000000000000000000000000000000000..05e3e59a73a8ed9ab78048e6b9522cc95f2fd82d
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/38.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>38.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\38.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>492</width>
+		<height>348</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>208</xmin>
+			<ymin>25</ymin>
+			<xmax>428</xmax>
+			<ymax>311</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/39.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/39.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/39.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/39.xml
new file mode 100644
index 0000000000000000000000000000000000000000..a66d6d4be73f0712cce547d699e0e885348eeae5
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/39.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>39.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\39.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>469</width>
+		<height>368</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>58</xmin>
+			<ymin>120</ymin>
+			<xmax>329</xmax>
+			<ymax>307</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/4.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/4.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/4.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/4.xml
new file mode 100644
index 0000000000000000000000000000000000000000..6ae7150fa5243bdb61f61c2fc0d7cd3bb73770a2
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/4.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>4.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\4.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>381</width>
+		<height>454</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>14</xmin>
+			<ymin>25</ymin>
+			<xmax>365</xmax>
+			<ymax>432</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/40.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/40.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/40.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/40.xml
new file mode 100644
index 0000000000000000000000000000000000000000..54d5cd8d132225e1425f56d52a92b60c73357d51
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/40.xml	
@@ -0,0 +1,146 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>40.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\40.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>507</width>
+		<height>338</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>51</xmin>
+			<ymin>125</ymin>
+			<xmax>149</xmax>
+			<ymax>300</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>147</xmin>
+			<ymin>108</ymin>
+			<xmax>230</xmax>
+			<ymax>247</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>54</xmin>
+			<ymin>12</ymin>
+			<xmax>84</xmax>
+			<ymax>57</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>150</xmin>
+			<ymin>10</ymin>
+			<xmax>172</xmax>
+			<ymax>48</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>218</xmin>
+			<ymin>8</ymin>
+			<xmax>241</xmax>
+			<ymax>43</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>444</xmin>
+			<ymin>43</ymin>
+			<xmax>479</xmax>
+			<ymax>101</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>305</xmin>
+			<ymin>156</ymin>
+			<xmax>407</xmax>
+			<ymax>254</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>275</xmin>
+			<ymin>10</ymin>
+			<xmax>293</xmax>
+			<ymax>38</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>485</xmin>
+			<ymin>35</ymin>
+			<xmax>507</xmax>
+			<ymax>82</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>314</xmin>
+			<ymin>9</ymin>
+			<xmax>328</xmax>
+			<ymax>35</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>354</xmin>
+			<ymin>9</ymin>
+			<xmax>368</xmax>
+			<ymax>33</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/41.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/41.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/41.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/41.xml
new file mode 100644
index 0000000000000000000000000000000000000000..c49262c9497ef61c6eb8ce92a5ed051d82039973
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/41.xml	
@@ -0,0 +1,122 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>41.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\41.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>529</width>
+		<height>327</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>45</xmin>
+			<ymin>155</ymin>
+			<xmax>149</xmax>
+			<ymax>319</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>152</xmin>
+			<ymin>128</ymin>
+			<xmax>241</xmax>
+			<ymax>277</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>228</xmin>
+			<ymin>106</ymin>
+			<xmax>311</xmax>
+			<ymax>247</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>295</xmin>
+			<ymin>90</ymin>
+			<xmax>368</xmax>
+			<ymax>227</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>339</xmin>
+			<ymin>72</ymin>
+			<xmax>407</xmax>
+			<ymax>200</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>381</xmin>
+			<ymin>61</ymin>
+			<xmax>444</xmax>
+			<ymax>184</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>420</xmin>
+			<ymin>47</ymin>
+			<xmax>477</xmax>
+			<ymax>165</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>464</xmin>
+			<ymin>35</ymin>
+			<xmax>520</xmax>
+			<ymax>145</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>496</xmin>
+			<ymin>22</ymin>
+			<xmax>529</xmax>
+			<ymax>123</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/42.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/42.jpg
new file mode 100644
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/42.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/42.xml
new file mode 100644
index 0000000000000000000000000000000000000000..695e55e16778cb35ea2b99b4b5be45e154091f3e
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/42.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>42.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\42.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>338</width>
+		<height>507</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>66</xmin>
+			<ymin>11</ymin>
+			<xmax>293</xmax>
+			<ymax>492</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/43.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/43.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/43.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/43.xml
new file mode 100644
index 0000000000000000000000000000000000000000..0feb64e1c1383bfefcce73cc193b471ca31805ff
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/43.xml	
@@ -0,0 +1,62 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>43.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\43.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>507</width>
+		<height>338</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>126</xmin>
+			<ymin>267</ymin>
+			<xmax>139</xmax>
+			<ymax>285</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>391</xmin>
+			<ymin>261</ymin>
+			<xmax>402</xmax>
+			<ymax>282</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>264</xmin>
+			<ymin>260</ymin>
+			<xmax>277</xmax>
+			<ymax>281</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>194</xmin>
+			<ymin>261</ymin>
+			<xmax>208</xmax>
+			<ymax>282</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/44.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/44.jpg
new file mode 100644
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/44.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/44.xml
new file mode 100644
index 0000000000000000000000000000000000000000..24bc004131fd66c71d6b739a48f1e22b89a604d3
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/44.xml	
@@ -0,0 +1,50 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>44.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\44.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>337</width>
+		<height>508</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>132</xmin>
+			<ymin>37</ymin>
+			<xmax>275</xmax>
+			<ymax>302</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>2</xmin>
+			<ymin>283</ymin>
+			<xmax>99</xmax>
+			<ymax>508</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>107</xmin>
+			<ymin>1</ymin>
+			<xmax>187</xmax>
+			<ymax>95</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/45.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/45.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/45.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/45.xml
new file mode 100644
index 0000000000000000000000000000000000000000..9fcb50f9ca3c734fa55c734247bb72039fb79423
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/45.xml	
@@ -0,0 +1,98 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>45.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\45.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>594</width>
+		<height>396</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>256</xmin>
+			<ymin>264</ymin>
+			<xmax>308</xmax>
+			<ymax>347</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>115</xmin>
+			<ymin>249</ymin>
+			<xmax>154</xmax>
+			<ymax>317</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>15</xmin>
+			<ymin>240</ymin>
+			<xmax>53</xmax>
+			<ymax>293</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>507</xmin>
+			<ymin>283</ymin>
+			<xmax>573</xmax>
+			<ymax>391</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>347</xmin>
+			<ymin>192</ymin>
+			<xmax>359</xmax>
+			<ymax>209</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>289</xmin>
+			<ymin>190</ymin>
+			<xmax>302</xmax>
+			<ymax>209</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>102</xmin>
+			<ymin>194</ymin>
+			<xmax>111</xmax>
+			<ymax>206</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/46.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/46.jpg
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Binary files /dev/null and b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/46.jpg differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/46.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/46.xml
new file mode 100644
index 0000000000000000000000000000000000000000..04029dac98ce8d36867c69aa932cc8ef5a6da83c
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/46.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>46.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\46.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>338</width>
+		<height>507</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>132</xmin>
+			<ymin>162</ymin>
+			<xmax>304</xmax>
+			<ymax>478</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/47.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/47.jpg
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Binary files /dev/null and b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/47.jpg differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/47.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/47.xml
new file mode 100644
index 0000000000000000000000000000000000000000..3e3aa3d1a0ccc16f7695e16d4739e5028c9a5c5e
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/47.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>47.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\47.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>478</width>
+		<height>359</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>103</xmin>
+			<ymin>11</ymin>
+			<xmax>384</xmax>
+			<ymax>322</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/48.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/48.jpg
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Binary files /dev/null and b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/48.jpg differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/48.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/48.xml
new file mode 100644
index 0000000000000000000000000000000000000000..68b1fdd321847c9d06d4e7e8c343be44866c4465
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/48.xml	
@@ -0,0 +1,62 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>48.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\48.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>514</width>
+		<height>333</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>16</xmin>
+			<ymin>23</ymin>
+			<xmax>80</xmax>
+			<ymax>135</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>113</xmin>
+			<ymin>34</ymin>
+			<xmax>190</xmax>
+			<ymax>164</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>375</xmin>
+			<ymin>90</ymin>
+			<xmax>506</xmax>
+			<ymax>291</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>205</xmin>
+			<ymin>165</ymin>
+			<xmax>363</xmax>
+			<ymax>301</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/49.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/49.jpg
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Binary files /dev/null and b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/49.jpg differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/49.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/49.xml
new file mode 100644
index 0000000000000000000000000000000000000000..7a3761ffd47ef6872354eae63391148063096287
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/49.xml	
@@ -0,0 +1,38 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>49.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\49.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>594</width>
+		<height>334</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>27</xmin>
+			<ymin>44</ymin>
+			<xmax>212</xmax>
+			<ymax>283</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>369</xmin>
+			<ymin>9</ymin>
+			<xmax>589</xmax>
+			<ymax>292</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/5.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/5.jpg
new file mode 100644
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Binary files /dev/null and b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/5.jpg differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/5.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/5.xml
new file mode 100644
index 0000000000000000000000000000000000000000..5b963a42f1c4e195a6b3a367d1566c326ea3f5e6
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/5.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>5.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\5.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>337</width>
+		<height>508</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>5</xmin>
+			<ymin>38</ymin>
+			<xmax>336</xmax>
+			<ymax>478</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/50.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/50.jpg
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Binary files /dev/null and b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/50.jpg differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/50.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/50.xml
new file mode 100644
index 0000000000000000000000000000000000000000..c396b6031b259f2cf9a0af8bdef12cd59eb45594
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/50.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>50.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\50.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>337</width>
+		<height>508</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>53</xmin>
+			<ymin>47</ymin>
+			<xmax>259</xmax>
+			<ymax>456</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/51.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/51.jpg
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Binary files /dev/null and b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/51.jpg differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/51.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/51.xml
new file mode 100644
index 0000000000000000000000000000000000000000..16d57f31d918a02bda72df976e45fcce5e519c3a
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/51.xml	
@@ -0,0 +1,74 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>51.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\51.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>594</width>
+		<height>401</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>378</xmin>
+			<ymin>246</ymin>
+			<xmax>411</xmax>
+			<ymax>286</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>22</xmin>
+			<ymin>292</ymin>
+			<xmax>79</xmax>
+			<ymax>368</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>178</xmin>
+			<ymin>257</ymin>
+			<xmax>205</xmax>
+			<ymax>309</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>113</xmin>
+			<ymin>263</ymin>
+			<xmax>145</xmax>
+			<ymax>321</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>79</xmin>
+			<ymin>275</ymin>
+			<xmax>117</xmax>
+			<ymax>339</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/52.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/52.jpg
new file mode 100644
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Binary files /dev/null and b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/52.jpg differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/52.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/52.xml
new file mode 100644
index 0000000000000000000000000000000000000000..80a8ffbf2fac7cd8c62660bea200651d6512c30c
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/52.xml	
@@ -0,0 +1,38 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>52.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\52.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>170</width>
+		<height>106</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>1</xmin>
+			<ymin>1</ymin>
+			<xmax>97</xmax>
+			<ymax>103</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>35</xmin>
+			<ymin>16</ymin>
+			<xmax>161</xmax>
+			<ymax>104</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/53.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/53.jpg
new file mode 100644
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Binary files /dev/null and b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/53.jpg differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/53.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/53.xml
new file mode 100644
index 0000000000000000000000000000000000000000..a86fb83d76693f31bba0053ec6e6486359612f54
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/53.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>53.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\53.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>114</width>
+		<height>170</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>17</xmin>
+			<ymin>19</ymin>
+			<xmax>92</xmax>
+			<ymax>158</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/54.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/54.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/54.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/54.xml
new file mode 100644
index 0000000000000000000000000000000000000000..7e059368607fb0a24950911c4330ea9ef7128013
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/54.xml	
@@ -0,0 +1,62 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>54.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\54.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>416</width>
+		<height>416</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>309</xmin>
+			<ymin>158</ymin>
+			<xmax>383</xmax>
+			<ymax>264</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>13</xmin>
+			<ymin>161</ymin>
+			<xmax>85</xmax>
+			<ymax>264</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>34</xmin>
+			<ymin>90</ymin>
+			<xmax>95</xmax>
+			<ymax>166</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>276</xmin>
+			<ymin>82</ymin>
+			<xmax>324</xmax>
+			<ymax>156</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/55.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/55.jpg
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Binary files /dev/null and b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/55.jpg differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/55.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/55.xml
new file mode 100644
index 0000000000000000000000000000000000000000..870124701ab6dad222cde82a5b397f7ba9df93b3
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/55.xml	
@@ -0,0 +1,38 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>55.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\55.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>478</width>
+		<height>359</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>177</xmin>
+			<ymin>44</ymin>
+			<xmax>355</xmax>
+			<ymax>286</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>377</xmin>
+			<ymin>164</ymin>
+			<xmax>448</xmax>
+			<ymax>261</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/56.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/56.jpg
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Binary files /dev/null and b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/56.jpg differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/56.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/56.xml
new file mode 100644
index 0000000000000000000000000000000000000000..256b78685e7e218cb345c084807faa46516a4a09
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/56.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>56.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\56.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>422</width>
+		<height>407</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>79</xmin>
+			<ymin>10</ymin>
+			<xmax>306</xmax>
+			<ymax>314</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/57.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/57.jpg
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Binary files /dev/null and b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/57.jpg differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/57.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/57.xml
new file mode 100644
index 0000000000000000000000000000000000000000..70c7754a80ddad37f42d2c093976b8d357f52bb3
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/57.xml	
@@ -0,0 +1,86 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>57.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\57.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>414</width>
+		<height>414</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>23</xmin>
+			<ymin>229</ymin>
+			<xmax>158</xmax>
+			<ymax>394</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>171</xmin>
+			<ymin>190</ymin>
+			<xmax>267</xmax>
+			<ymax>307</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>257</xmin>
+			<ymin>168</ymin>
+			<xmax>330</xmax>
+			<ymax>260</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>311</xmin>
+			<ymin>155</ymin>
+			<xmax>372</xmax>
+			<ymax>229</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>355</xmin>
+			<ymin>145</ymin>
+			<xmax>401</xmax>
+			<ymax>208</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>388</xmin>
+			<ymin>138</ymin>
+			<xmax>414</xmax>
+			<ymax>191</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/58.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/58.jpg
new file mode 100644
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Binary files /dev/null and b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/58.jpg differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/58.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/58.xml
new file mode 100644
index 0000000000000000000000000000000000000000..d7e5b8a5b216526919367bd777880bcca9c78b82
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/58.xml	
@@ -0,0 +1,38 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>58.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\58.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>369</width>
+		<height>464</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>34</xmin>
+			<ymin>171</ymin>
+			<xmax>193</xmax>
+			<ymax>433</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>148</xmin>
+			<ymin>174</ymin>
+			<xmax>339</xmax>
+			<ymax>284</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/59.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/59.jpg
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Binary files /dev/null and b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/59.jpg differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/59.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/59.xml
new file mode 100644
index 0000000000000000000000000000000000000000..57a3eb5e6d8ed886b287f24400929c36df87820a
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/59.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>59.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\59.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>359</width>
+		<height>478</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>53</xmin>
+			<ymin>57</ymin>
+			<xmax>292</xmax>
+			<ymax>434</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/6.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/6.jpg
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Binary files /dev/null and b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/6.jpg differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/6.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/6.xml
new file mode 100644
index 0000000000000000000000000000000000000000..30164432ff2f73dbdb5dd172572dc99ea59458e7
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/6.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>6.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\6.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>337</width>
+		<height>508</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>6</xmin>
+			<ymin>39</ymin>
+			<xmax>332</xmax>
+			<ymax>468</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/60.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/60.jpg
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Binary files /dev/null and b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/60.jpg differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/60.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/60.xml
new file mode 100644
index 0000000000000000000000000000000000000000..fd820d8c78151e52d0ffb4fc0bbc1cac9db90e2e
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/60.xml	
@@ -0,0 +1,86 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>60.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\60.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>482</width>
+		<height>355</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>50</xmin>
+			<ymin>148</ymin>
+			<xmax>195</xmax>
+			<ymax>337</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>212</xmin>
+			<ymin>109</ymin>
+			<xmax>317</xmax>
+			<ymax>235</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>306</xmin>
+			<ymin>81</ymin>
+			<xmax>389</xmax>
+			<ymax>182</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>368</xmin>
+			<ymin>67</ymin>
+			<xmax>434</xmax>
+			<ymax>149</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>422</xmin>
+			<ymin>54</ymin>
+			<xmax>468</xmax>
+			<ymax>125</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>454</xmin>
+			<ymin>48</ymin>
+			<xmax>482</xmax>
+			<ymax>105</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/61.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/61.jpg
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Binary files /dev/null and b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/61.jpg differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/61.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/61.xml
new file mode 100644
index 0000000000000000000000000000000000000000..234d206c51ad03671c775d091469d1f1a03c1427
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/61.xml	
@@ -0,0 +1,50 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>61.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\61.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>507</width>
+		<height>338</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>57</xmin>
+			<ymin>168</ymin>
+			<xmax>166</xmax>
+			<ymax>319</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>69</xmin>
+			<ymin>125</ymin>
+			<xmax>143</xmax>
+			<ymax>225</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>96</xmin>
+			<ymin>73</ymin>
+			<xmax>139</xmax>
+			<ymax>134</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/62.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/62.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/62.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/62.xml
new file mode 100644
index 0000000000000000000000000000000000000000..6c5e86da09fdff2eda112e4ac2dbefe0b21e3aac
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/62.xml	
@@ -0,0 +1,50 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>62.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\62.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>478</width>
+		<height>359</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>249</xmin>
+			<ymin>182</ymin>
+			<xmax>346</xmax>
+			<ymax>303</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>107</xmin>
+			<ymin>189</ymin>
+			<xmax>223</xmax>
+			<ymax>272</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>48</xmin>
+			<ymin>155</ymin>
+			<xmax>128</xmax>
+			<ymax>259</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/63.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/63.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/63.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/63.xml
new file mode 100644
index 0000000000000000000000000000000000000000..8a22ec396b8b851a79249caf1320e387e82e050f
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/63.xml	
@@ -0,0 +1,62 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>63.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\63.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>478</width>
+		<height>359</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>292</xmin>
+			<ymin>202</ymin>
+			<xmax>398</xmax>
+			<ymax>293</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>130</xmin>
+			<ymin>168</ymin>
+			<xmax>222</xmax>
+			<ymax>270</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>52</xmin>
+			<ymin>210</ymin>
+			<xmax>153</xmax>
+			<ymax>335</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>183</xmin>
+			<ymin>153</ymin>
+			<xmax>256</xmax>
+			<ymax>218</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/64.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/64.jpg
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Binary files /dev/null and b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/64.jpg differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/64.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/64.xml
new file mode 100644
index 0000000000000000000000000000000000000000..856ed467912123196397451dcd2a4559c0a84b61
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/64.xml	
@@ -0,0 +1,62 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>64.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\64.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>359</width>
+		<height>479</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>171</xmin>
+			<ymin>26</ymin>
+			<xmax>254</xmax>
+			<ymax>178</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>2</xmin>
+			<ymin>179</ymin>
+			<xmax>189</xmax>
+			<ymax>474</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>252</xmin>
+			<ymin>1</ymin>
+			<xmax>301</xmax>
+			<ymax>85</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>286</xmin>
+			<ymin>1</ymin>
+			<xmax>322</xmax>
+			<ymax>43</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/65.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/65.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/65.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/65.xml
new file mode 100644
index 0000000000000000000000000000000000000000..802484edee2522128e25d51cbe357c1673f5f584
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/65.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>65.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\65.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>339</width>
+		<height>509</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>24</xmin>
+			<ymin>58</ymin>
+			<xmax>318</xmax>
+			<ymax>471</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/66.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/66.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/66.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/66.xml
new file mode 100644
index 0000000000000000000000000000000000000000..16f99d0bc13f4cb7a6fae51962fdccf12564eee3
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/66.xml	
@@ -0,0 +1,38 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>66.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\66.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>1024</width>
+		<height>680</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>69</xmin>
+			<ymin>257</ymin>
+			<xmax>329</xmax>
+			<ymax>648</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>649</xmin>
+			<ymin>239</ymin>
+			<xmax>922</xmax>
+			<ymax>621</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/67.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/67.jpg
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Binary files /dev/null and b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/67.jpg differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/67.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/67.xml
new file mode 100644
index 0000000000000000000000000000000000000000..f0843121c0f2f17800fad45f4e84d9280d95d58a
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/67.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>67.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\67.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>356</width>
+		<height>481</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>106</xmin>
+			<ymin>88</ymin>
+			<xmax>241</xmax>
+			<ymax>316</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/68.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/68.jpg
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Binary files /dev/null and b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/68.jpg differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/68.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/68.xml
new file mode 100644
index 0000000000000000000000000000000000000000..7cb60fc813748d489aa9e7022bc9f1beb14b4cd2
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/68.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>68.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\68.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>478</width>
+		<height>359</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>221</xmin>
+			<ymin>110</ymin>
+			<xmax>259</xmax>
+			<ymax>179</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/69.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/69.jpg
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Binary files /dev/null and b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/69.jpg differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/69.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/69.xml
new file mode 100644
index 0000000000000000000000000000000000000000..b13e86158c90b864f77db1ff8af3b28c750e8dfc
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/69.xml	
@@ -0,0 +1,38 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>69.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\69.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>478</width>
+		<height>359</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>28</xmin>
+			<ymin>30</ymin>
+			<xmax>240</xmax>
+			<ymax>302</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>178</xmin>
+			<ymin>99</ymin>
+			<xmax>452</xmax>
+			<ymax>312</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/7.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/7.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/7.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/7.xml
new file mode 100644
index 0000000000000000000000000000000000000000..24cdb64b5e9aeeeab9b3272251a371bd210d5955
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/7.xml	
@@ -0,0 +1,98 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>7.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\7.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>507</width>
+		<height>338</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>280</xmin>
+			<ymin>45</ymin>
+			<xmax>438</xmax>
+			<ymax>274</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>79</xmin>
+			<ymin>150</ymin>
+			<xmax>100</xmax>
+			<ymax>186</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>167</xmin>
+			<ymin>154</ymin>
+			<xmax>187</xmax>
+			<ymax>184</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>203</xmin>
+			<ymin>152</ymin>
+			<xmax>219</xmax>
+			<ymax>187</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>255</xmin>
+			<ymin>114</ymin>
+			<xmax>310</xmax>
+			<ymax>221</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>245</xmin>
+			<ymin>137</ymin>
+			<xmax>269</xmax>
+			<ymax>201</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>231</xmin>
+			<ymin>145</ymin>
+			<xmax>250</xmax>
+			<ymax>195</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/70.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/70.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/70.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/70.xml
new file mode 100644
index 0000000000000000000000000000000000000000..0f9684ad168cc5b2038e22b2b070e9da5e38feee
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/70.xml	
@@ -0,0 +1,62 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>70.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\70.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>363</width>
+		<height>473</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>73</xmin>
+			<ymin>232</ymin>
+			<xmax>155</xmax>
+			<ymax>405</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>247</xmin>
+			<ymin>72</ymin>
+			<xmax>323</xmax>
+			<ymax>223</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>188</xmin>
+			<ymin>111</ymin>
+			<xmax>251</xmax>
+			<ymax>269</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>126</xmin>
+			<ymin>166</ymin>
+			<xmax>203</xmax>
+			<ymax>330</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/71.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/71.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/71.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/71.xml
new file mode 100644
index 0000000000000000000000000000000000000000..64c7bf357912453265f66eb997af4b62b6a32e6f
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/71.xml	
@@ -0,0 +1,50 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>71.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\71.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>412</width>
+		<height>415</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>177</xmin>
+			<ymin>69</ymin>
+			<xmax>373</xmax>
+			<ymax>393</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>65</xmin>
+			<ymin>166</ymin>
+			<xmax>125</xmax>
+			<ymax>255</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>342</xmin>
+			<ymin>152</ymin>
+			<xmax>412</xmax>
+			<ymax>265</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/72.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/72.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/72.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/72.xml
new file mode 100644
index 0000000000000000000000000000000000000000..0d61d6cbdfd581ccfb81b5b97ceffd1645af1d96
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/72.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>72.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\72.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>338</width>
+		<height>507</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>129</xmin>
+			<ymin>159</ymin>
+			<xmax>301</xmax>
+			<ymax>476</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/73.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/73.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/73.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/73.xml
new file mode 100644
index 0000000000000000000000000000000000000000..0f8f8107f3f7eb860d2bee386606af66ba915800
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/73.xml	
@@ -0,0 +1,98 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>73.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\73.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>604</width>
+		<height>283</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>268</xmin>
+			<ymin>113</ymin>
+			<xmax>315</xmax>
+			<ymax>171</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>185</xmin>
+			<ymin>113</ymin>
+			<xmax>233</xmax>
+			<ymax>170</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>103</xmin>
+			<ymin>114</ymin>
+			<xmax>150</xmax>
+			<ymax>170</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>15</xmin>
+			<ymin>114</ymin>
+			<xmax>65</xmax>
+			<ymax>170</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>351</xmin>
+			<ymin>114</ymin>
+			<xmax>402</xmax>
+			<ymax>171</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>440</xmin>
+			<ymin>114</ymin>
+			<xmax>487</xmax>
+			<ymax>171</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>528</xmin>
+			<ymin>116</ymin>
+			<xmax>575</xmax>
+			<ymax>171</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/74.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/74.jpg
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Binary files /dev/null and b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/74.jpg differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/74.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/74.xml
new file mode 100644
index 0000000000000000000000000000000000000000..6488c5d6052cf3612c807ce9c8a2691c889bedbe
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/74.xml	
@@ -0,0 +1,74 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>74.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\74.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>509</width>
+		<height>339</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>50</xmin>
+			<ymin>1</ymin>
+			<xmax>183</xmax>
+			<ymax>188</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>253</xmin>
+			<ymin>64</ymin>
+			<xmax>366</xmax>
+			<ymax>268</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>339</xmin>
+			<ymin>7</ymin>
+			<xmax>429</xmax>
+			<ymax>179</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>127</xmin>
+			<ymin>99</ymin>
+			<xmax>259</xmax>
+			<ymax>335</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>425</xmin>
+			<ymin>4</ymin>
+			<xmax>509</xmax>
+			<ymax>171</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/75.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/75.jpg
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Binary files /dev/null and b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/75.jpg differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/75.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/75.xml
new file mode 100644
index 0000000000000000000000000000000000000000..efa46fc84bf45338d5f506073e56a6f3b19b6ed7
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/75.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>75.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\75.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>416</width>
+		<height>416</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>52</xmin>
+			<ymin>16</ymin>
+			<xmax>354</xmax>
+			<ymax>393</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/76.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/76.jpg
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Binary files /dev/null and b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/76.jpg differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/76.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/76.xml
new file mode 100644
index 0000000000000000000000000000000000000000..63c748a0e852e2fa76d55778d2cd946a5781f3b3
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/76.xml	
@@ -0,0 +1,38 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>76.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\76.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>492</width>
+		<height>348</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>80</xmin>
+			<ymin>49</ymin>
+			<xmax>295</xmax>
+			<ymax>298</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>216</xmin>
+			<ymin>81</ymin>
+			<xmax>399</xmax>
+			<ymax>271</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/77.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/77.jpg
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Binary files /dev/null and b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/77.jpg differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/77.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/77.xml
new file mode 100644
index 0000000000000000000000000000000000000000..0a2ab193a08b57922c41fff5876dc24c38cfed0b
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/77.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>77.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\77.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>387</width>
+		<height>443</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>8</xmin>
+			<ymin>1</ymin>
+			<xmax>378</xmax>
+			<ymax>441</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/78.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/78.jpg
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Binary files /dev/null and b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/78.jpg differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/78.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/78.xml
new file mode 100644
index 0000000000000000000000000000000000000000..ca56d792b10f822085cf171a72295f8a66256072
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/78.xml	
@@ -0,0 +1,38 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>78.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\78.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>337</width>
+		<height>506</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>138</xmin>
+			<ymin>164</ymin>
+			<xmax>306</xmax>
+			<ymax>457</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>53</xmin>
+			<ymin>162</ymin>
+			<xmax>109</xmax>
+			<ymax>252</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/79.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/79.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/79.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/79.xml
new file mode 100644
index 0000000000000000000000000000000000000000..438ff7841bfccfdf4a44f9c9ffac90bf7f0c3bd8
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/79.xml	
@@ -0,0 +1,62 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>79.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\79.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>505</width>
+		<height>339</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>317</xmin>
+			<ymin>106</ymin>
+			<xmax>435</xmax>
+			<ymax>319</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>267</xmin>
+			<ymin>140</ymin>
+			<xmax>340</xmax>
+			<ymax>266</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>239</xmin>
+			<ymin>167</ymin>
+			<xmax>272</xmax>
+			<ymax>233</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>419</xmin>
+			<ymin>164</ymin>
+			<xmax>470</xmax>
+			<ymax>253</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/8.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/8.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/8.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/8.xml
new file mode 100644
index 0000000000000000000000000000000000000000..f0bfc8c54c84f3fe60fafb5dc905943ff5940370
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/8.xml	
@@ -0,0 +1,74 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>8.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\8.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>507</width>
+		<height>338</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>332</xmin>
+			<ymin>52</ymin>
+			<xmax>460</xmax>
+			<ymax>295</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>207</xmin>
+			<ymin>131</ymin>
+			<xmax>242</xmax>
+			<ymax>205</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>169</xmin>
+			<ymin>147</ymin>
+			<xmax>192</xmax>
+			<ymax>191</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>155</xmin>
+			<ymin>155</ymin>
+			<xmax>169</xmax>
+			<ymax>183</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>47</xmin>
+			<ymin>167</ymin>
+			<xmax>52</xmax>
+			<ymax>174</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/80.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/80.jpg
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Binary files /dev/null and b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/80.jpg differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/80.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/80.xml
new file mode 100644
index 0000000000000000000000000000000000000000..6fda51997bc8d2cea21b50c137b24bca933c66f8
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/80.xml	
@@ -0,0 +1,62 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>80.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\80.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>366</width>
+		<height>467</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>98</xmin>
+			<ymin>211</ymin>
+			<xmax>182</xmax>
+			<ymax>357</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>99</xmin>
+			<ymin>202</ymin>
+			<xmax>148</xmax>
+			<ymax>299</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>134</xmin>
+			<ymin>241</ymin>
+			<xmax>228</xmax>
+			<ymax>465</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>108</xmin>
+			<ymin>195</ymin>
+			<xmax>133</xmax>
+			<ymax>249</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/81.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/81.jpg
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Binary files /dev/null and b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/81.jpg differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/81.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/81.xml
new file mode 100644
index 0000000000000000000000000000000000000000..0c4bb3847fb79e9c08f6ecca23529f971d20e66e
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/81.xml	
@@ -0,0 +1,62 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>81.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\81.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>414</width>
+		<height>414</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>78</xmin>
+			<ymin>55</ymin>
+			<xmax>260</xmax>
+			<ymax>308</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>257</xmin>
+			<ymin>121</ymin>
+			<xmax>356</xmax>
+			<ymax>272</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>233</xmin>
+			<ymin>180</ymin>
+			<xmax>278</xmax>
+			<ymax>247</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>43</xmin>
+			<ymin>160</ymin>
+			<xmax>123</xmax>
+			<ymax>253</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/82.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/82.jpg
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Binary files /dev/null and b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/82.jpg differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/82.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/82.xml
new file mode 100644
index 0000000000000000000000000000000000000000..08e829f79ce8bd927ea31c698b72a3efa73b7ffa
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/82.xml	
@@ -0,0 +1,62 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>82.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\82.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>514</width>
+		<height>333</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>105</xmin>
+			<ymin>33</ymin>
+			<xmax>191</xmax>
+			<ymax>163</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>13</xmin>
+			<ymin>25</ymin>
+			<xmax>83</xmax>
+			<ymax>136</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>376</xmin>
+			<ymin>90</ymin>
+			<xmax>506</xmax>
+			<ymax>292</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>205</xmin>
+			<ymin>168</ymin>
+			<xmax>363</xmax>
+			<ymax>296</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/83.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/83.jpg
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Binary files /dev/null and b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/83.jpg differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/83.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/83.xml
new file mode 100644
index 0000000000000000000000000000000000000000..1797700a59aab71846ac7cf94a08535dfd379470
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/83.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>83.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\83.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>509</width>
+		<height>339</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>252</xmin>
+			<ymin>1</ymin>
+			<xmax>509</xmax>
+			<ymax>336</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/84.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/84.jpg
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Binary files /dev/null and b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/84.jpg differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/84.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/84.xml
new file mode 100644
index 0000000000000000000000000000000000000000..b55dd0d3ef3f0e9d7c24aa9be7b8417fb256779d
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/84.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>84.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\84.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>509</width>
+		<height>339</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>192</xmin>
+			<ymin>189</ymin>
+			<xmax>333</xmax>
+			<ymax>325</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/85.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/85.jpg
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Binary files /dev/null and b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/85.jpg differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/85.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/85.xml
new file mode 100644
index 0000000000000000000000000000000000000000..2b14df2b422c4309989d3315e635809905275f8e
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/85.xml	
@@ -0,0 +1,62 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>85.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\85.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>509</width>
+		<height>339</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>146</xmin>
+			<ymin>71</ymin>
+			<xmax>228</xmax>
+			<ymax>215</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>294</xmin>
+			<ymin>28</ymin>
+			<xmax>407</xmax>
+			<ymax>187</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>38</xmin>
+			<ymin>109</ymin>
+			<xmax>114</xmax>
+			<ymax>232</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>408</xmin>
+			<ymin>73</ymin>
+			<xmax>497</xmax>
+			<ymax>222</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/86.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/86.jpg
new file mode 100644
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Binary files /dev/null and b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/86.jpg differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/86.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/86.xml
new file mode 100644
index 0000000000000000000000000000000000000000..1c4e504ab35cd39b5812c65a351236812066c7ef
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/86.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>86.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\86.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>507</width>
+		<height>338</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>193</xmin>
+			<ymin>12</ymin>
+			<xmax>457</xmax>
+			<ymax>275</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/87.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/87.jpg
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Binary files /dev/null and b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/87.jpg differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/87.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/87.xml
new file mode 100644
index 0000000000000000000000000000000000000000..0648a0c8b0f96d2381e2cc2b3fd1904ce05da10f
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/87.xml	
@@ -0,0 +1,62 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>87.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\87.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>339</width>
+		<height>506</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>159</xmin>
+			<ymin>223</ymin>
+			<xmax>287</xmax>
+			<ymax>454</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>39</xmin>
+			<ymin>40</ymin>
+			<xmax>77</xmax>
+			<ymax>91</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>114</xmin>
+			<ymin>5</ymin>
+			<xmax>143</xmax>
+			<ymax>43</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>165</xmin>
+			<ymin>4</ymin>
+			<xmax>178</xmax>
+			<ymax>25</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/88.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/88.jpg
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Binary files /dev/null and b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/88.jpg differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/88.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/88.xml
new file mode 100644
index 0000000000000000000000000000000000000000..f61101ba72552659d6c4480315f3c96bf656c086
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/88.xml	
@@ -0,0 +1,62 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>88.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\88.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>478</width>
+		<height>359</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>1</xmin>
+			<ymin>52</ymin>
+			<xmax>106</xmax>
+			<ymax>355</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>204</xmin>
+			<ymin>125</ymin>
+			<xmax>234</xmax>
+			<ymax>193</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>231</xmin>
+			<ymin>139</ymin>
+			<xmax>257</xmax>
+			<ymax>180</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>249</xmin>
+			<ymin>145</ymin>
+			<xmax>261</xmax>
+			<ymax>170</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/89.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/89.jpg
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Binary files /dev/null and b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/89.jpg differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/89.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/89.xml
new file mode 100644
index 0000000000000000000000000000000000000000..b953d1ecb6ff011c9f2ade1f3ac49ab87e56a7cd
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/89.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>89.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\89.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>338</width>
+		<height>507</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>62</xmin>
+			<ymin>10</ymin>
+			<xmax>291</xmax>
+			<ymax>492</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/9.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/9.jpg
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Binary files /dev/null and b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/9.jpg differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/9.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/9.xml
new file mode 100644
index 0000000000000000000000000000000000000000..fd785042d0bb01466c4f88da84cf1199d672ef50
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/9.xml	
@@ -0,0 +1,50 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>9.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\9.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>507</width>
+		<height>338</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>359</xmin>
+			<ymin>94</ymin>
+			<xmax>484</xmax>
+			<ymax>293</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>276</xmin>
+			<ymin>104</ymin>
+			<xmax>362</xmax>
+			<ymax>257</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>120</xmin>
+			<ymin>168</ymin>
+			<xmax>302</xmax>
+			<ymax>293</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/90.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/90.jpg
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Binary files /dev/null and b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/90.jpg differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/90.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/90.xml
new file mode 100644
index 0000000000000000000000000000000000000000..e40077788f69f2e374503b6d5d0bea7abaec493e
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/90.xml	
@@ -0,0 +1,98 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>90.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\90.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>535</width>
+		<height>322</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>220</xmin>
+			<ymin>203</ymin>
+			<xmax>258</xmax>
+			<ymax>250</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>121</xmin>
+			<ymin>213</ymin>
+			<xmax>135</xmax>
+			<ymax>241</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>70</xmin>
+			<ymin>203</ymin>
+			<xmax>103</xmax>
+			<ymax>244</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>6</xmin>
+			<ymin>213</ymin>
+			<xmax>30</xmax>
+			<ymax>243</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>308</xmin>
+			<ymin>177</ymin>
+			<xmax>359</xmax>
+			<ymax>261</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>396</xmin>
+			<ymin>221</ymin>
+			<xmax>415</xmax>
+			<ymax>241</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>458</xmin>
+			<ymin>224</ymin>
+			<xmax>470</xmax>
+			<ymax>241</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/91.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/91.jpg
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Binary files /dev/null and b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/91.jpg differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/91.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/91.xml
new file mode 100644
index 0000000000000000000000000000000000000000..84472c691496745cc5faab9a0176fa1f32bbff79
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/91.xml	
@@ -0,0 +1,50 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>91.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\91.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>553</width>
+		<height>311</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>148</xmin>
+			<ymin>27</ymin>
+			<xmax>262</xmax>
+			<ymax>161</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>257</xmin>
+			<ymin>1</ymin>
+			<xmax>350</xmax>
+			<ymax>137</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>1</xmin>
+			<ymin>1</ymin>
+			<xmax>122</xmax>
+			<ymax>88</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/92.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/92.jpg
new file mode 100644
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Binary files /dev/null and b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/92.jpg differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/92.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/92.xml
new file mode 100644
index 0000000000000000000000000000000000000000..f7f18b524196b3a3caea2cbb6998a95ee8cf8ad7
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/92.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>92.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\92.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>359</width>
+		<height>479</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>69</xmin>
+			<ymin>190</ymin>
+			<xmax>223</xmax>
+			<ymax>389</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/93.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/93.jpg
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Binary files /dev/null and b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/93.jpg differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/93.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/93.xml
new file mode 100644
index 0000000000000000000000000000000000000000..3805ae278700271f60d861a76293f88457c2e235
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/93.xml	
@@ -0,0 +1,62 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>93.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\93.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>337</width>
+		<height>507</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>91</xmin>
+			<ymin>39</ymin>
+			<xmax>178</xmax>
+			<ymax>161</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>3</xmin>
+			<ymin>28</ymin>
+			<xmax>76</xmax>
+			<ymax>128</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>223</xmin>
+			<ymin>60</ymin>
+			<xmax>327</xmax>
+			<ymax>210</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>259</xmin>
+			<ymin>4</ymin>
+			<xmax>290</xmax>
+			<ymax>53</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/94.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/94.jpg
new file mode 100644
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Binary files /dev/null and b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/94.jpg differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/94.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/94.xml
new file mode 100644
index 0000000000000000000000000000000000000000..7337dca0b4b3370976e3e7227bb8778a8b687e5f
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/94.xml	
@@ -0,0 +1,62 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>94.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\94.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>507</width>
+		<height>339</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>218</xmin>
+			<ymin>24</ymin>
+			<xmax>397</xmax>
+			<ymax>294</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>130</xmin>
+			<ymin>100</ymin>
+			<xmax>183</xmax>
+			<ymax>188</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>111</xmin>
+			<ymin>120</ymin>
+			<xmax>138</xmax>
+			<ymax>167</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>101</xmin>
+			<ymin>125</ymin>
+			<xmax>122</xmax>
+			<ymax>156</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/95.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/95.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/95.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/95.xml
new file mode 100644
index 0000000000000000000000000000000000000000..c29900471df0f112799cada8017beefbc06bb433
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/95.xml	
@@ -0,0 +1,110 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>95.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\95.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>510</width>
+		<height>337</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>312</xmin>
+			<ymin>168</ymin>
+			<xmax>402</xmax>
+			<ymax>325</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>430</xmin>
+			<ymin>23</ymin>
+			<xmax>462</xmax>
+			<ymax>75</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>366</xmin>
+			<ymin>22</ymin>
+			<xmax>399</xmax>
+			<ymax>77</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>311</xmin>
+			<ymin>23</ymin>
+			<xmax>341</xmax>
+			<ymax>75</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>292</xmin>
+			<ymin>117</ymin>
+			<xmax>366</xmax>
+			<ymax>248</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>280</xmin>
+			<ymin>92</ymin>
+			<xmax>333</xmax>
+			<ymax>202</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>121</xmin>
+			<ymin>16</ymin>
+			<xmax>155</xmax>
+			<ymax>65</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>65</xmin>
+			<ymin>16</ymin>
+			<xmax>106</xmax>
+			<ymax>66</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/96.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/96.jpg
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Binary files /dev/null and b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/96.jpg differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/96.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/96.xml
new file mode 100644
index 0000000000000000000000000000000000000000..23a5390244b0c27ab51f5126142e283bd16b3850
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/96.xml	
@@ -0,0 +1,62 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>96.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\96.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>505</width>
+		<height>342</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>317</xmin>
+			<ymin>113</ymin>
+			<xmax>373</xmax>
+			<ymax>171</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>189</xmin>
+			<ymin>94</ymin>
+			<xmax>255</xmax>
+			<ymax>176</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>218</xmin>
+			<ymin>183</ymin>
+			<xmax>421</xmax>
+			<ymax>320</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>263</xmin>
+			<ymin>96</ymin>
+			<xmax>289</xmax>
+			<ymax>137</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/97.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/97.jpg
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Binary files /dev/null and b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/97.jpg differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/97.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/97.xml
new file mode 100644
index 0000000000000000000000000000000000000000..3c2c10de8201fd621b1d60bd819d4c7c097c8296
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/97.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>97.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\97.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>432</width>
+		<height>400</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>97</xmin>
+			<ymin>1</ymin>
+			<xmax>166</xmax>
+			<ymax>108</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/98.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/98.jpg
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Binary files /dev/null and b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/98.jpg differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/98.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/98.xml
new file mode 100644
index 0000000000000000000000000000000000000000..8e096381babb4e30abbbb42573548353f5cfa28d
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/98.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>98.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\98.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>509</width>
+		<height>339</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>179</xmin>
+			<ymin>43</ymin>
+			<xmax>255</xmax>
+			<ymax>167</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/99.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/99.jpg
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Binary files /dev/null and b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/99.jpg differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/99.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/99.xml
new file mode 100644
index 0000000000000000000000000000000000000000..fcae8467e04e5d6e324cd8b075ab9e0affe9c2e5
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/99.xml	
@@ -0,0 +1,86 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>99.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\99.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>476</width>
+		<height>362</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>201</xmin>
+			<ymin>170</ymin>
+			<xmax>294</xmax>
+			<ymax>330</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>231</xmin>
+			<ymin>156</ymin>
+			<xmax>306</xmax>
+			<ymax>297</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>266</xmin>
+			<ymin>139</ymin>
+			<xmax>342</xmax>
+			<ymax>265</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>308</xmin>
+			<ymin>128</ymin>
+			<xmax>370</xmax>
+			<ymax>241</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>328</xmin>
+			<ymin>117</ymin>
+			<xmax>388</xmax>
+			<ymax>217</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>372</xmin>
+			<ymin>101</ymin>
+			<xmax>421</xmax>
+			<ymax>186</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/1.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/1.jpg
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Binary files /dev/null and b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/1.jpg differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/1.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/1.xml
new file mode 100644
index 0000000000000000000000000000000000000000..d159c342a9eff91ecfc2b2bff46ab2af8237db2f
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/1.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>1.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\1.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>414</width>
+		<height>414</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>112</xmin>
+			<ymin>35</ymin>
+			<xmax>309</xmax>
+			<ymax>327</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/10.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/10.jpg
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Binary files /dev/null and b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/10.jpg differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/10.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/10.xml
new file mode 100644
index 0000000000000000000000000000000000000000..97cce271f9014d863bade26e13d8ffe88d572943
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/10.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>10.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\10.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>508</width>
+		<height>337</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>248</xmin>
+			<ymin>39</ymin>
+			<xmax>436</xmax>
+			<ymax>328</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/100.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/100.jpg
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Binary files /dev/null and b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/100.jpg differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/100.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/100.xml
new file mode 100644
index 0000000000000000000000000000000000000000..a4f1e281d26fbb19a991c5b4a2aec1439a86652f
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/100.xml	
@@ -0,0 +1,62 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>100.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\100.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>509</width>
+		<height>339</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>40</xmin>
+			<ymin>66</ymin>
+			<xmax>229</xmax>
+			<ymax>319</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>197</xmin>
+			<ymin>73</ymin>
+			<xmax>312</xmax>
+			<ymax>248</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>323</xmin>
+			<ymin>76</ymin>
+			<xmax>387</xmax>
+			<ymax>171</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>174</xmin>
+			<ymin>80</ymin>
+			<xmax>243</xmax>
+			<ymax>201</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/101.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/101.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/101.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/101.xml
new file mode 100644
index 0000000000000000000000000000000000000000..958c11046fa5c40a05c221f6776edf732e33ef6a
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/101.xml	
@@ -0,0 +1,62 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>101.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\101.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>336</width>
+		<height>509</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>242</xmin>
+			<ymin>383</ymin>
+			<xmax>267</xmax>
+			<ymax>436</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>171</xmin>
+			<ymin>377</ymin>
+			<xmax>213</xmax>
+			<ymax>437</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>107</xmin>
+			<ymin>381</ymin>
+			<xmax>143</xmax>
+			<ymax>434</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>32</xmin>
+			<ymin>378</ymin>
+			<xmax>73</xmax>
+			<ymax>436</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/102.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/102.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/102.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/102.xml
new file mode 100644
index 0000000000000000000000000000000000000000..0b575db6936b0321e789d26d19ac36ba7a6ff930
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/102.xml	
@@ -0,0 +1,74 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>102.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\102.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>359</width>
+		<height>479</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>190</xmin>
+			<ymin>275</ymin>
+			<xmax>295</xmax>
+			<ymax>430</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>212</xmin>
+			<ymin>260</ymin>
+			<xmax>297</xmax>
+			<ymax>384</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>234</xmin>
+			<ymin>234</ymin>
+			<xmax>305</xmax>
+			<ymax>345</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>264</xmin>
+			<ymin>226</ymin>
+			<xmax>324</xmax>
+			<ymax>319</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>283</xmin>
+			<ymin>214</ymin>
+			<xmax>326</xmax>
+			<ymax>289</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/103.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/103.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/103.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/103.xml
new file mode 100644
index 0000000000000000000000000000000000000000..c717ab0e7d044d5ff6b1ef6360c90f5f50017f4a
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/103.xml	
@@ -0,0 +1,62 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>103.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\103.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>525</width>
+		<height>329</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>124</xmin>
+			<ymin>127</ymin>
+			<xmax>228</xmax>
+			<ymax>296</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>6</xmin>
+			<ymin>69</ymin>
+			<xmax>93</xmax>
+			<ymax>194</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>255</xmin>
+			<ymin>36</ymin>
+			<xmax>321</xmax>
+			<ymax>145</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>423</xmin>
+			<ymin>81</ymin>
+			<xmax>519</xmax>
+			<ymax>223</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/104.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/104.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/104.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/104.xml
new file mode 100644
index 0000000000000000000000000000000000000000..1c2a4ab2cd64de1abb20dbe9000820543b202a39
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/104.xml	
@@ -0,0 +1,62 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>104.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\104.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>449</width>
+		<height>382</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>127</xmin>
+			<ymin>71</ymin>
+			<xmax>261</xmax>
+			<ymax>300</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>70</xmin>
+			<ymin>96</ymin>
+			<xmax>168</xmax>
+			<ymax>288</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>281</xmin>
+			<ymin>31</ymin>
+			<xmax>449</xmax>
+			<ymax>338</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>255</xmin>
+			<ymin>87</ymin>
+			<xmax>350</xmax>
+			<ymax>289</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/105.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/105.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/105.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/105.xml
new file mode 100644
index 0000000000000000000000000000000000000000..b19e2dba6aef0ea08aa7a2701e7dd202bf9a830b
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/105.xml	
@@ -0,0 +1,86 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>105.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\105.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>445</width>
+		<height>594</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>1</xmin>
+			<ymin>57</ymin>
+			<xmax>182</xmax>
+			<ymax>569</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>44</xmin>
+			<ymin>53</ymin>
+			<xmax>237</xmax>
+			<ymax>479</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>133</xmin>
+			<ymin>66</ymin>
+			<xmax>296</xmax>
+			<ymax>409</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>204</xmin>
+			<ymin>62</ymin>
+			<xmax>333</xmax>
+			<ymax>364</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>277</xmin>
+			<ymin>43</ymin>
+			<xmax>397</xmax>
+			<ymax>315</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>330</xmin>
+			<ymin>33</ymin>
+			<xmax>429</xmax>
+			<ymax>275</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/106.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/106.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/106.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/106.xml
new file mode 100644
index 0000000000000000000000000000000000000000..e0d0731395371c618affdf81c108809c4e1ad1fb
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/106.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>106.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\106.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>170</width>
+		<height>108</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>5</xmin>
+			<ymin>23</ymin>
+			<xmax>102</xmax>
+			<ymax>86</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/107.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/107.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/107.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/107.xml
new file mode 100644
index 0000000000000000000000000000000000000000..7df09c28143339caef9663afa46044eacf8aec12
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/107.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>107.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\107.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>493</width>
+		<height>594</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>189</xmin>
+			<ymin>224</ymin>
+			<xmax>316</xmax>
+			<ymax>441</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/108.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/108.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/108.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/108.xml
new file mode 100644
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/108.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>108.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\108.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>395</width>
+		<height>594</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>49</xmin>
+			<ymin>37</ymin>
+			<xmax>353</xmax>
+			<ymax>587</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/109.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/109.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/109.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/109.xml
new file mode 100644
index 0000000000000000000000000000000000000000..7c8b5113f509fd1910898a775309dbd7e37b5a93
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/109.xml	
@@ -0,0 +1,98 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>109.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\109.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>507</width>
+		<height>338</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>298</xmin>
+			<ymin>224</ymin>
+			<xmax>333</xmax>
+			<ymax>273</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>246</xmin>
+			<ymin>225</ymin>
+			<xmax>275</xmax>
+			<ymax>274</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>186</xmin>
+			<ymin>224</ymin>
+			<xmax>219</xmax>
+			<ymax>273</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>134</xmin>
+			<ymin>226</ymin>
+			<xmax>168</xmax>
+			<ymax>269</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>348</xmin>
+			<ymin>226</ymin>
+			<xmax>377</xmax>
+			<ymax>270</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>379</xmin>
+			<ymin>226</ymin>
+			<xmax>407</xmax>
+			<ymax>269</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>99</xmin>
+			<ymin>225</ymin>
+			<xmax>131</xmax>
+			<ymax>267</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/11.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/11.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/11.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/11.xml
new file mode 100644
index 0000000000000000000000000000000000000000..4f2ab8385285dd4e1750c7607e44353d17e93df8
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/11.xml	
@@ -0,0 +1,134 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>11.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\11.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>508</width>
+		<height>338</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>400</xmin>
+			<ymin>20</ymin>
+			<xmax>508</xmax>
+			<ymax>255</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>294</xmin>
+			<ymin>19</ymin>
+			<xmax>405</xmax>
+			<ymax>205</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>226</xmin>
+			<ymin>18</ymin>
+			<xmax>312</xmax>
+			<ymax>172</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>164</xmin>
+			<ymin>15</ymin>
+			<xmax>243</xmax>
+			<ymax>148</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>107</xmin>
+			<ymin>12</ymin>
+			<xmax>172</xmax>
+			<ymax>125</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>84</xmin>
+			<ymin>13</ymin>
+			<xmax>135</xmax>
+			<ymax>114</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>58</xmin>
+			<ymin>11</ymin>
+			<xmax>99</xmax>
+			<ymax>102</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>310</xmin>
+			<ymin>7</ymin>
+			<xmax>341</xmax>
+			<ymax>64</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>423</xmin>
+			<ymin>8</ymin>
+			<xmax>464</xmax>
+			<ymax>79</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>171</xmin>
+			<ymin>8</ymin>
+			<xmax>194</xmax>
+			<ymax>46</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/110.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/110.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/110.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/110.xml
new file mode 100644
index 0000000000000000000000000000000000000000..ca3674f25b2a0174db035fe033d5cf1191c0bf64
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/110.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>110.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\110.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>413</width>
+		<height>416</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>109</xmin>
+			<ymin>61</ymin>
+			<xmax>269</xmax>
+			<ymax>324</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/111.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/111.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/111.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/111.xml
new file mode 100644
index 0000000000000000000000000000000000000000..c193e113c287d37b2e3e40ae61d3e357e2c7b69e
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/111.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>111.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\111.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>359</width>
+		<height>478</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>1</xmin>
+			<ymin>18</ymin>
+			<xmax>148</xmax>
+			<ymax>270</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/112.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/112.jpg
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Binary files /dev/null and b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/112.jpg differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/112.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/112.xml
new file mode 100644
index 0000000000000000000000000000000000000000..6b5ae328e4255250e9da0f97637d182e5bdaa2dd
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/112.xml	
@@ -0,0 +1,38 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>112.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\112.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>507</width>
+		<height>337</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>233</xmin>
+			<ymin>186</ymin>
+			<xmax>303</xmax>
+			<ymax>304</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>31</xmin>
+			<ymin>194</ymin>
+			<xmax>108</xmax>
+			<ymax>312</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/113.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/113.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/113.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/113.xml
new file mode 100644
index 0000000000000000000000000000000000000000..f215a8926395304615b57553496ff2916527559e
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/113.xml	
@@ -0,0 +1,38 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>113.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\113.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>507</width>
+		<height>337</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>166</xmin>
+			<ymin>210</ymin>
+			<xmax>206</xmax>
+			<ymax>273</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>52</xmin>
+			<ymin>212</ymin>
+			<xmax>95</xmax>
+			<ymax>275</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/114.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/114.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/114.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/114.xml
new file mode 100644
index 0000000000000000000000000000000000000000..63c7af1c30678797a8487a0db6c79f503546bbf2
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/114.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>114.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\114.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>359</width>
+		<height>478</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>113</xmin>
+			<ymin>207</ymin>
+			<xmax>280</xmax>
+			<ymax>461</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/115.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/115.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/115.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/115.xml
new file mode 100644
index 0000000000000000000000000000000000000000..bf71ccf9a473f81f5cb5cd4938f212e8ddfbd312
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/115.xml	
@@ -0,0 +1,74 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>115.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\115.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>507</width>
+		<height>338</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>384</xmin>
+			<ymin>235</ymin>
+			<xmax>439</xmax>
+			<ymax>316</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>459</xmin>
+			<ymin>234</ymin>
+			<xmax>507</xmax>
+			<ymax>315</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>172</xmin>
+			<ymin>213</ymin>
+			<xmax>197</xmax>
+			<ymax>262</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>40</xmin>
+			<ymin>194</ymin>
+			<xmax>61</xmax>
+			<ymax>230</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>144</xmin>
+			<ymin>209</ymin>
+			<xmax>173</xmax>
+			<ymax>259</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/116.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/116.jpg
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Binary files /dev/null and b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/116.jpg differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/116.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/116.xml
new file mode 100644
index 0000000000000000000000000000000000000000..ccb5344abb84b37663afcb63de06581f18f280e3
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/116.xml	
@@ -0,0 +1,74 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>116.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\116.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>416</width>
+		<height>412</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>193</xmin>
+			<ymin>216</ymin>
+			<xmax>281</xmax>
+			<ymax>397</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>139</xmin>
+			<ymin>183</ymin>
+			<xmax>217</xmax>
+			<ymax>331</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>245</xmin>
+			<ymin>138</ymin>
+			<xmax>334</xmax>
+			<ymax>182</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>132</xmin>
+			<ymin>117</ymin>
+			<xmax>186</xmax>
+			<ymax>236</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>188</xmin>
+			<ymin>114</ymin>
+			<xmax>241</xmax>
+			<ymax>210</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/117.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/117.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/117.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/117.xml
new file mode 100644
index 0000000000000000000000000000000000000000..2f72f24c9191dcead939676baa69cd3f7f328c9b
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/117.xml	
@@ -0,0 +1,74 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>117.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\117.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>478</width>
+		<height>359</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>156</xmin>
+			<ymin>83</ymin>
+			<xmax>255</xmax>
+			<ymax>278</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>82</xmin>
+			<ymin>36</ymin>
+			<xmax>170</xmax>
+			<ymax>206</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>256</xmin>
+			<ymin>74</ymin>
+			<xmax>359</xmax>
+			<ymax>251</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>158</xmin>
+			<ymin>14</ymin>
+			<xmax>241</xmax>
+			<ymax>160</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>246</xmin>
+			<ymin>28</ymin>
+			<xmax>338</xmax>
+			<ymax>175</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/118.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/118.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/118.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/118.xml
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/118.xml	
@@ -0,0 +1,74 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>118.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\118.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>1024</width>
+		<height>665</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>671</xmin>
+			<ymin>439</ymin>
+			<xmax>773</xmax>
+			<ymax>587</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>537</xmin>
+			<ymin>440</ymin>
+			<xmax>653</xmax>
+			<ymax>588</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>346</xmin>
+			<ymin>442</ymin>
+			<xmax>457</xmax>
+			<ymax>572</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>95</xmin>
+			<ymin>445</ymin>
+			<xmax>214</xmax>
+			<ymax>565</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>1</xmin>
+			<ymin>442</ymin>
+			<xmax>59</xmax>
+			<ymax>553</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/119.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/119.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/119.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/119.xml
new file mode 100644
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/119.xml	
@@ -0,0 +1,38 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>119.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\119.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>509</width>
+		<height>339</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>73</xmin>
+			<ymin>197</ymin>
+			<xmax>127</xmax>
+			<ymax>303</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>101</xmin>
+			<ymin>188</ymin>
+			<xmax>151</xmax>
+			<ymax>276</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/12.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/12.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/12.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/12.xml
new file mode 100644
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/12.xml	
@@ -0,0 +1,50 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>12.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\12.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>359</width>
+		<height>479</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>121</xmin>
+			<ymin>115</ymin>
+			<xmax>340</xmax>
+			<ymax>426</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>104</xmin>
+			<ymin>175</ymin>
+			<xmax>192</xmax>
+			<ymax>314</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>74</xmin>
+			<ymin>196</ymin>
+			<xmax>118</xmax>
+			<ymax>289</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/120.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/120.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/120.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/120.xml
new file mode 100644
index 0000000000000000000000000000000000000000..1c0cab18d7ae5a65a8e117f40bedd15886b4bf49
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/120.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>120.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\120.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>506</width>
+		<height>337</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>264</xmin>
+			<ymin>213</ymin>
+			<xmax>314</xmax>
+			<ymax>308</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/121.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/121.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/121.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/121.xml
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/121.xml	
@@ -0,0 +1,86 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>121.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\121.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>507</width>
+		<height>337</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>84</xmin>
+			<ymin>150</ymin>
+			<xmax>175</xmax>
+			<ymax>337</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>214</xmin>
+			<ymin>117</ymin>
+			<xmax>233</xmax>
+			<ymax>153</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>281</xmin>
+			<ymin>121</ymin>
+			<xmax>314</xmax>
+			<ymax>174</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>259</xmin>
+			<ymin>117</ymin>
+			<xmax>283</xmax>
+			<ymax>159</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>235</xmin>
+			<ymin>118</ymin>
+			<xmax>252</xmax>
+			<ymax>151</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>186</xmin>
+			<ymin>117</ymin>
+			<xmax>199</xmax>
+			<ymax>137</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/122.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/122.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/122.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/122.xml
new file mode 100644
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/122.xml	
@@ -0,0 +1,38 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>122.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\122.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>339</width>
+		<height>510</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>145</xmin>
+			<ymin>377</ymin>
+			<xmax>186</xmax>
+			<ymax>445</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>251</xmin>
+			<ymin>357</ymin>
+			<xmax>263</xmax>
+			<ymax>392</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/123.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/123.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/123.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/123.xml
new file mode 100644
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/123.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>123.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\123.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>508</width>
+		<height>337</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>51</xmin>
+			<ymin>44</ymin>
+			<xmax>123</xmax>
+			<ymax>240</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/124.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/124.jpg
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/124.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>124.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\124.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>359</width>
+		<height>479</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>125</xmin>
+			<ymin>259</ymin>
+			<xmax>225</xmax>
+			<ymax>397</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/125.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/125.jpg
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/125.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>125.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\125.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>507</width>
+		<height>338</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>195</xmin>
+			<ymin>93</ymin>
+			<xmax>309</xmax>
+			<ymax>264</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/126.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/126.jpg
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/126.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>126.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\126.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>338</width>
+		<height>507</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>12</xmin>
+			<ymin>31</ymin>
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+			<ymax>477</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/127.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/127.jpg
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/127.xml	
@@ -0,0 +1,122 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>127.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\127.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>529</width>
+		<height>327</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>50</xmin>
+			<ymin>154</ymin>
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+			<ymax>320</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>147</xmin>
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+			<xmax>241</xmax>
+			<ymax>279</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>225</xmin>
+			<ymin>104</ymin>
+			<xmax>312</xmax>
+			<ymax>250</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>286</xmin>
+			<ymin>87</ymin>
+			<xmax>367</xmax>
+			<ymax>226</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>339</xmin>
+			<ymin>73</ymin>
+			<xmax>408</xmax>
+			<ymax>202</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>379</xmin>
+			<ymin>59</ymin>
+			<xmax>445</xmax>
+			<ymax>185</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>414</xmin>
+			<ymin>46</ymin>
+			<xmax>478</xmax>
+			<ymax>163</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>459</xmin>
+			<ymin>31</ymin>
+			<xmax>519</xmax>
+			<ymax>145</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>497</xmin>
+			<ymin>21</ymin>
+			<xmax>529</xmax>
+			<ymax>127</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/128.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/128.jpg
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/128.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>128.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\128.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>339</width>
+		<height>506</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>69</xmin>
+			<ymin>52</ymin>
+			<xmax>296</xmax>
+			<ymax>462</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/129.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/129.jpg
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/129.xml	
@@ -0,0 +1,74 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>129.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\129.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>509</width>
+		<height>339</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>314</xmin>
+			<ymin>202</ymin>
+			<xmax>386</xmax>
+			<ymax>318</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>93</xmin>
+			<ymin>207</ymin>
+			<xmax>158</xmax>
+			<ymax>313</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>403</xmin>
+			<ymin>217</ymin>
+			<xmax>446</xmax>
+			<ymax>285</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>276</xmin>
+			<ymin>215</ymin>
+			<xmax>322</xmax>
+			<ymax>291</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>17</xmin>
+			<ymin>223</ymin>
+			<xmax>47</xmax>
+			<ymax>273</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/13.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/13.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/13.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/13.xml
new file mode 100644
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/13.xml	
@@ -0,0 +1,62 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>13.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\13.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>414</width>
+		<height>414</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>233</xmin>
+			<ymin>132</ymin>
+			<xmax>374</xmax>
+			<ymax>368</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>154</xmin>
+			<ymin>95</ymin>
+			<xmax>258</xmax>
+			<ymax>270</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>89</xmin>
+			<ymin>79</ymin>
+			<xmax>176</xmax>
+			<ymax>219</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>33</xmin>
+			<ymin>65</ymin>
+			<xmax>108</xmax>
+			<ymax>183</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/130.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/130.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/130.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/130.xml
new file mode 100644
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/130.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>130.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\130.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>396</width>
+		<height>430</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>143</xmin>
+			<ymin>48</ymin>
+			<xmax>305</xmax>
+			<ymax>280</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/131.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/131.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/131.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/131.xml
new file mode 100644
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/131.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>131.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\131.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>375</width>
+		<height>458</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>71</xmin>
+			<ymin>40</ymin>
+			<xmax>296</xmax>
+			<ymax>407</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/132.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/132.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/132.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/132.xml
new file mode 100644
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/132.xml	
@@ -0,0 +1,38 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>132.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\132.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>507</width>
+		<height>338</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>60</xmin>
+			<ymin>63</ymin>
+			<xmax>198</xmax>
+			<ymax>300</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>177</xmin>
+			<ymin>139</ymin>
+			<xmax>380</xmax>
+			<ymax>300</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/133.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/133.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/133.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/133.xml
new file mode 100644
index 0000000000000000000000000000000000000000..286952000d06ca9843d44ac6927b25023ccbbd99
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/133.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>133.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\133.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>414</width>
+		<height>414</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>65</xmin>
+			<ymin>9</ymin>
+			<xmax>343</xmax>
+			<ymax>399</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/134.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/134.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/134.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/134.xml
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/134.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>134.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\134.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>414</width>
+		<height>414</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>87</xmin>
+			<ymin>6</ymin>
+			<xmax>331</xmax>
+			<ymax>409</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/135.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/135.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/135.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/135.xml
new file mode 100644
index 0000000000000000000000000000000000000000..47ff1be42fc3e7096f279a7e5b6c0be952c624b1
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/135.xml	
@@ -0,0 +1,50 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>135.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\135.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>478</width>
+		<height>359</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>142</xmin>
+			<ymin>133</ymin>
+			<xmax>285</xmax>
+			<ymax>303</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>323</xmin>
+			<ymin>84</ymin>
+			<xmax>443</xmax>
+			<ymax>213</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>40</xmin>
+			<ymin>111</ymin>
+			<xmax>171</xmax>
+			<ymax>245</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/136.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/136.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/136.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/136.xml
new file mode 100644
index 0000000000000000000000000000000000000000..deea19282e6f408d6d097797be297c58181f5578
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/136.xml	
@@ -0,0 +1,110 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>136.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\136.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>837</width>
+		<height>1024</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>485</xmin>
+			<ymin>480</ymin>
+			<xmax>568</xmax>
+			<ymax>621</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>570</xmin>
+			<ymin>412</ymin>
+			<xmax>639</xmax>
+			<ymax>528</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>644</xmin>
+			<ymin>361</ymin>
+			<xmax>704</xmax>
+			<ymax>451</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>714</xmin>
+			<ymin>308</ymin>
+			<xmax>764</xmax>
+			<ymax>385</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>773</xmin>
+			<ymin>273</ymin>
+			<xmax>817</xmax>
+			<ymax>334</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>264</xmin>
+			<ymin>615</ymin>
+			<xmax>390</xmax>
+			<ymax>848</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>126</xmin>
+			<ymin>707</ymin>
+			<xmax>284</xmax>
+			<ymax>985</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>391</xmin>
+			<ymin>642</ymin>
+			<xmax>536</xmax>
+			<ymax>766</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/137.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/137.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/137.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/137.xml
new file mode 100644
index 0000000000000000000000000000000000000000..5bc04c6a295eeb36113a32e575defe7db9ea83dc
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/137.xml	
@@ -0,0 +1,50 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>137.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\137.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>507</width>
+		<height>338</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>117</xmin>
+			<ymin>168</ymin>
+			<xmax>303</xmax>
+			<ymax>293</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>349</xmin>
+			<ymin>90</ymin>
+			<xmax>492</xmax>
+			<ymax>297</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>283</xmin>
+			<ymin>100</ymin>
+			<xmax>365</xmax>
+			<ymax>259</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/138.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/138.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/138.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/138.xml
new file mode 100644
index 0000000000000000000000000000000000000000..161be36dfd6b0e9ec41456dd28229e5ae5a9aa3f
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/138.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>138.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\138.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>381</width>
+		<height>454</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>6</xmin>
+			<ymin>20</ymin>
+			<xmax>367</xmax>
+			<ymax>434</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/139.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/139.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/139.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/139.xml
new file mode 100644
index 0000000000000000000000000000000000000000..3a9b7b1544a11328c9dfb89ea523ad1c8c7cf6de
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/139.xml	
@@ -0,0 +1,62 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>139.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\139.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>340</width>
+		<height>508</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>17</xmin>
+			<ymin>264</ymin>
+			<xmax>120</xmax>
+			<ymax>460</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>4</xmin>
+			<ymin>251</ymin>
+			<xmax>55</xmax>
+			<ymax>385</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>208</xmin>
+			<ymin>262</ymin>
+			<xmax>314</xmax>
+			<ymax>460</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>269</xmin>
+			<ymin>244</ymin>
+			<xmax>338</xmax>
+			<ymax>379</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/14.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/14.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/14.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/14.xml
new file mode 100644
index 0000000000000000000000000000000000000000..ba95eccc852d8f09b42e811e6cfa8410276402e8
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/14.xml	
@@ -0,0 +1,50 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>14.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\14.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>359</width>
+		<height>479</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>118</xmin>
+			<ymin>113</ymin>
+			<xmax>338</xmax>
+			<ymax>423</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>95</xmin>
+			<ymin>175</ymin>
+			<xmax>190</xmax>
+			<ymax>313</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>78</xmin>
+			<ymin>196</ymin>
+			<xmax>120</xmax>
+			<ymax>283</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/140.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/140.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/140.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/140.xml
new file mode 100644
index 0000000000000000000000000000000000000000..80e5d6637db504bc0d76836d7136b1569ee4ac43
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/140.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>140.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\140.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>337</width>
+		<height>506</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>183</xmin>
+			<ymin>166</ymin>
+			<xmax>314</xmax>
+			<ymax>388</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/141.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/141.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/141.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/141.xml
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/141.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>141.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\141.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>337</width>
+		<height>508</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>49</xmin>
+			<ymin>45</ymin>
+			<xmax>260</xmax>
+			<ymax>459</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/142.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/142.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/142.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/142.xml
new file mode 100644
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/142.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>142.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\142.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>337</width>
+		<height>506</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>145</xmin>
+			<ymin>291</ymin>
+			<xmax>192</xmax>
+			<ymax>369</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/143.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/143.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/143.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/143.xml
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/143.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>143.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\143.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>338</width>
+		<height>507</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>130</xmin>
+			<ymin>155</ymin>
+			<xmax>304</xmax>
+			<ymax>477</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/144.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/144.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/144.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/144.xml
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/144.xml	
@@ -0,0 +1,62 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>144.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\144.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>416</width>
+		<height>416</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>158</xmin>
+			<ymin>178</ymin>
+			<xmax>203</xmax>
+			<ymax>247</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>165</xmin>
+			<ymin>339</ymin>
+			<xmax>230</xmax>
+			<ymax>416</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>158</xmin>
+			<ymin>154</ymin>
+			<xmax>184</xmax>
+			<ymax>196</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>186</xmin>
+			<ymin>139</ymin>
+			<xmax>196</xmax>
+			<ymax>153</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/145.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/145.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/145.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/145.xml
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/145.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>145.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\145.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>506</width>
+		<height>341</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>273</xmin>
+			<ymin>45</ymin>
+			<xmax>442</xmax>
+			<ymax>337</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/146.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/146.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/146.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/146.xml
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/146.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>146.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\146.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>336</width>
+		<height>509</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>80</xmin>
+			<ymin>119</ymin>
+			<xmax>336</xmax>
+			<ymax>509</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/147.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/147.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/147.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/147.xml
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/147.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>147.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\147.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>347</width>
+		<height>491</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>40</xmin>
+			<ymin>126</ymin>
+			<xmax>240</xmax>
+			<ymax>450</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/148.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/148.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/148.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/148.xml
new file mode 100644
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/148.xml	
@@ -0,0 +1,38 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>148.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\148.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>507</width>
+		<height>337</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>10</xmin>
+			<ymin>16</ymin>
+			<xmax>175</xmax>
+			<ymax>327</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>339</xmin>
+			<ymin>18</ymin>
+			<xmax>507</xmax>
+			<ymax>328</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/149.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/149.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/149.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/149.xml
new file mode 100644
index 0000000000000000000000000000000000000000..cca92371bac194de8c02bf0f9096e1d625a62a31
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/149.xml	
@@ -0,0 +1,50 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>149.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\149.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>509</width>
+		<height>339</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>10</xmin>
+			<ymin>100</ymin>
+			<xmax>94</xmax>
+			<ymax>216</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>131</xmin>
+			<ymin>104</ymin>
+			<xmax>221</xmax>
+			<ymax>214</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>483</xmin>
+			<ymin>140</ymin>
+			<xmax>509</xmax>
+			<ymax>212</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/15.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/15.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/15.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/15.xml
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/15.xml	
@@ -0,0 +1,38 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>15.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\15.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>478</width>
+		<height>359</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>186</xmin>
+			<ymin>49</ymin>
+			<xmax>357</xmax>
+			<ymax>287</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>381</xmin>
+			<ymin>166</ymin>
+			<xmax>449</xmax>
+			<ymax>263</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/150.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/150.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/150.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/150.xml
new file mode 100644
index 0000000000000000000000000000000000000000..2995b80b8d861da6272db85aa08ee34553c693f5
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/150.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>150.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\150.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>359</width>
+		<height>479</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>112</xmin>
+			<ymin>97</ymin>
+			<xmax>244</xmax>
+			<ymax>345</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/151.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/151.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/151.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/151.xml
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/151.xml	
@@ -0,0 +1,62 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>151.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\151.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>479</width>
+		<height>359</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>190</xmin>
+			<ymin>89</ymin>
+			<xmax>270</xmax>
+			<ymax>217</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
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+		<difficult>0</difficult>
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+			<xmin>236</xmin>
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+			<xmax>289</xmax>
+			<ymax>123</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>73</xmin>
+			<ymin>244</ymin>
+			<xmax>146</xmax>
+			<ymax>359</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>249</xmin>
+			<ymin>11</ymin>
+			<xmax>291</xmax>
+			<ymax>77</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/152.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/152.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/152.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/152.xml
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/152.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>152.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\152.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>509</width>
+		<height>339</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>274</xmin>
+			<ymin>57</ymin>
+			<xmax>391</xmax>
+			<ymax>196</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/153.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/153.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/153.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/153.xml
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/153.xml	
@@ -0,0 +1,74 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>153.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\153.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>509</width>
+		<height>339</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>91</xmin>
+			<ymin>172</ymin>
+			<xmax>172</xmax>
+			<ymax>325</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>319</xmin>
+			<ymin>174</ymin>
+			<xmax>422</xmax>
+			<ymax>333</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>422</xmin>
+			<ymin>156</ymin>
+			<xmax>452</xmax>
+			<ymax>207</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>339</xmin>
+			<ymin>152</ymin>
+			<xmax>374</xmax>
+			<ymax>207</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>487</xmin>
+			<ymin>156</ymin>
+			<xmax>509</xmax>
+			<ymax>204</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/154.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/154.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/154.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/154.xml
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/154.xml	
@@ -0,0 +1,50 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>154.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\154.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>359</width>
+		<height>479</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>110</xmin>
+			<ymin>68</ymin>
+			<xmax>178</xmax>
+			<ymax>179</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>217</xmin>
+			<ymin>180</ymin>
+			<xmax>340</xmax>
+			<ymax>299</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>135</xmin>
+			<ymin>143</ymin>
+			<xmax>224</xmax>
+			<ymax>244</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/155.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/155.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/155.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/155.xml
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/155.xml	
@@ -0,0 +1,62 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>155.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\155.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>479</width>
+		<height>359</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>120</xmin>
+			<ymin>67</ymin>
+			<xmax>222</xmax>
+			<ymax>253</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>1</xmin>
+			<ymin>59</ymin>
+			<xmax>102</xmax>
+			<ymax>256</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>296</xmin>
+			<ymin>77</ymin>
+			<xmax>400</xmax>
+			<ymax>253</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>392</xmin>
+			<ymin>74</ymin>
+			<xmax>479</xmax>
+			<ymax>260</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/156.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/156.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/156.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/156.xml
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/156.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>156.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\156.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>508</width>
+		<height>340</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>207</xmin>
+			<ymin>93</ymin>
+			<xmax>315</xmax>
+			<ymax>266</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/157.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/157.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/157.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/157.xml
new file mode 100644
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/157.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>157.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\157.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>552</width>
+		<height>312</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>47</xmin>
+			<ymin>1</ymin>
+			<xmax>313</xmax>
+			<ymax>265</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/158.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/158.jpg
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/158.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>158.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\158.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>1023</width>
+		<height>1024</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>683</xmin>
+			<ymin>420</ymin>
+			<xmax>965</xmax>
+			<ymax>934</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/159.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/159.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/159.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/159.xml
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/159.xml	
@@ -0,0 +1,86 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>159.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\159.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>339</width>
+		<height>508</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>52</xmin>
+			<ymin>243</ymin>
+			<xmax>150</xmax>
+			<ymax>460</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>172</xmin>
+			<ymin>184</ymin>
+			<xmax>237</xmax>
+			<ymax>315</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>230</xmin>
+			<ymin>152</ymin>
+			<xmax>270</xmax>
+			<ymax>235</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>257</xmin>
+			<ymin>139</ymin>
+			<xmax>285</xmax>
+			<ymax>203</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>279</xmin>
+			<ymin>128</ymin>
+			<xmax>300</xmax>
+			<ymax>180</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>290</xmin>
+			<ymin>126</ymin>
+			<xmax>307</xmax>
+			<ymax>165</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/16.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/16.jpg
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/16.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>16.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\16.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>478</width>
+		<height>359</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>39</xmin>
+			<ymin>101</ymin>
+			<xmax>215</xmax>
+			<ymax>332</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/160.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/160.jpg
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/160.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>160.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\160.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>479</width>
+		<height>359</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>147</xmin>
+			<ymin>7</ymin>
+			<xmax>258</xmax>
+			<ymax>161</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/161.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/161.jpg
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+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/161.xml	
@@ -0,0 +1,254 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>161.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\161.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>508</width>
+		<height>339</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>206</xmin>
+			<ymin>125</ymin>
+			<xmax>321</xmax>
+			<ymax>315</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>41</xmin>
+			<ymin>126</ymin>
+			<xmax>139</xmax>
+			<ymax>322</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>154</xmin>
+			<ymin>89</ymin>
+			<xmax>233</xmax>
+			<ymax>213</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>25</xmin>
+			<ymin>89</ymin>
+			<xmax>74</xmax>
+			<ymax>213</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>71</xmin>
+			<ymin>50</ymin>
+			<xmax>119</xmax>
+			<ymax>128</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>1</xmin>
+			<ymin>45</ymin>
+			<xmax>53</xmax>
+			<ymax>119</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>394</xmin>
+			<ymin>121</ymin>
+			<xmax>508</xmax>
+			<ymax>308</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>467</xmin>
+			<ymin>88</ymin>
+			<xmax>508</xmax>
+			<ymax>202</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>346</xmin>
+			<ymin>75</ymin>
+			<xmax>413</xmax>
+			<ymax>182</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>269</xmin>
+			<ymin>66</ymin>
+			<xmax>320</xmax>
+			<ymax>162</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>199</xmin>
+			<ymin>62</ymin>
+			<xmax>252</xmax>
+			<ymax>148</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>136</xmin>
+			<ymin>55</ymin>
+			<xmax>191</xmax>
+			<ymax>136</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>109</xmin>
+			<ymin>19</ymin>
+			<xmax>136</xmax>
+			<ymax>69</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>65</xmin>
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+			<ymax>69</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
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+		<difficult>0</difficult>
+		<bndbox>
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+			<xmax>52</xmax>
+			<ymax>69</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>227</xmin>
+			<ymin>22</ymin>
+			<xmax>256</xmax>
+			<ymax>72</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>185</xmin>
+			<ymin>20</ymin>
+			<xmax>213</xmax>
+			<ymax>73</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>367</xmin>
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+			<xmax>396</xmax>
+			<ymax>77</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>413</xmin>
+			<ymin>23</ymin>
+			<xmax>448</xmax>
+			<ymax>77</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>468</xmin>
+			<ymin>23</ymin>
+			<xmax>501</xmax>
+			<ymax>80</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/162.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/162.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/162.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/162.xml
new file mode 100644
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/162.xml	
@@ -0,0 +1,110 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>162.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\162.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>339</width>
+		<height>509</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>17</xmin>
+			<ymin>254</ymin>
+			<xmax>57</xmax>
+			<ymax>330</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>60</xmin>
+			<ymin>254</ymin>
+			<xmax>102</xmax>
+			<ymax>332</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>105</xmin>
+			<ymin>252</ymin>
+			<xmax>145</xmax>
+			<ymax>331</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>142</xmin>
+			<ymin>252</ymin>
+			<xmax>186</xmax>
+			<ymax>326</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>186</xmin>
+			<ymin>249</ymin>
+			<xmax>227</xmax>
+			<ymax>325</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>230</xmin>
+			<ymin>248</ymin>
+			<xmax>268</xmax>
+			<ymax>326</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>261</xmin>
+			<ymin>251</ymin>
+			<xmax>305</xmax>
+			<ymax>325</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>300</xmin>
+			<ymin>250</ymin>
+			<xmax>338</xmax>
+			<ymax>321</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/163.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/163.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/163.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/163.xml
new file mode 100644
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/163.xml	
@@ -0,0 +1,86 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>163.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\163.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>339</width>
+		<height>509</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>153</xmin>
+			<ymin>404</ymin>
+			<xmax>194</xmax>
+			<ymax>486</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>115</xmin>
+			<ymin>399</ymin>
+			<xmax>152</xmax>
+			<ymax>479</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>187</xmin>
+			<ymin>402</ymin>
+			<xmax>230</xmax>
+			<ymax>480</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>213</xmin>
+			<ymin>401</ymin>
+			<xmax>251</xmax>
+			<ymax>470</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>95</xmin>
+			<ymin>401</ymin>
+			<xmax>122</xmax>
+			<ymax>464</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>185</xmin>
+			<ymin>385</ymin>
+			<xmax>205</xmax>
+			<ymax>445</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/164.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/164.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/164.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/164.xml
new file mode 100644
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/164.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>164.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\164.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>359</width>
+		<height>479</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>201</xmin>
+			<ymin>144</ymin>
+			<xmax>331</xmax>
+			<ymax>406</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/165.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/165.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/165.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/165.xml
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/165.xml	
@@ -0,0 +1,98 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>165.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\165.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>509</width>
+		<height>339</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>200</xmin>
+			<ymin>59</ymin>
+			<xmax>239</xmax>
+			<ymax>156</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>90</xmin>
+			<ymin>175</ymin>
+			<xmax>162</xmax>
+			<ymax>337</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>245</xmin>
+			<ymin>21</ymin>
+			<xmax>271</xmax>
+			<ymax>76</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>456</xmin>
+			<ymin>112</ymin>
+			<xmax>494</xmax>
+			<ymax>234</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>392</xmin>
+			<ymin>50</ymin>
+			<xmax>426</xmax>
+			<ymax>131</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>356</xmin>
+			<ymin>12</ymin>
+			<xmax>377</xmax>
+			<ymax>61</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>344</xmin>
+			<ymin>1</ymin>
+			<xmax>357</xmax>
+			<ymax>32</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/166.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/166.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/166.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/166.xml
new file mode 100644
index 0000000000000000000000000000000000000000..c6fe5e9e0ef2e6be4efeb145aadc3a7c25437837
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/166.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>166.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\166.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>509</width>
+		<height>339</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>273</xmin>
+			<ymin>32</ymin>
+			<xmax>396</xmax>
+			<ymax>243</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/167.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/167.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/167.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/167.xml
new file mode 100644
index 0000000000000000000000000000000000000000..5494f8e6c259de10b84ac6e6316eb818d701e5f4
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/167.xml	
@@ -0,0 +1,62 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>167.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\167.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>512</width>
+		<height>336</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>7</xmin>
+			<ymin>150</ymin>
+			<xmax>95</xmax>
+			<ymax>298</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>136</xmin>
+			<ymin>152</ymin>
+			<xmax>205</xmax>
+			<ymax>302</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>338</xmin>
+			<ymin>155</ymin>
+			<xmax>416</xmax>
+			<ymax>300</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>412</xmin>
+			<ymin>158</ymin>
+			<xmax>496</xmax>
+			<ymax>297</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/168.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/168.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/168.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/168.xml
new file mode 100644
index 0000000000000000000000000000000000000000..ee3ba82bcba8909d3e316f20a573f9dd3b938c52
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/168.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>168.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\168.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>768</width>
+		<height>1024</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>273</xmin>
+			<ymin>421</ymin>
+			<xmax>471</xmax>
+			<ymax>901</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/169.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/169.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/169.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/169.xml
new file mode 100644
index 0000000000000000000000000000000000000000..a746a6ab81a0f3dc3a075134069c968eb5fda4fc
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/169.xml	
@@ -0,0 +1,50 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>169.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\169.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>359</width>
+		<height>479</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>60</xmin>
+			<ymin>304</ymin>
+			<xmax>174</xmax>
+			<ymax>474</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>160</xmin>
+			<ymin>217</ymin>
+			<xmax>246</xmax>
+			<ymax>356</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>238</xmin>
+			<ymin>164</ymin>
+			<xmax>320</xmax>
+			<ymax>296</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/17.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/17.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/17.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/17.xml
new file mode 100644
index 0000000000000000000000000000000000000000..0917b58198ec496f543687e542bc21fee5f174e0
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/17.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>17.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\17.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>338</width>
+		<height>507</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>63</xmin>
+			<ymin>14</ymin>
+			<xmax>294</xmax>
+			<ymax>490</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/170.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/170.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/170.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/170.xml
new file mode 100644
index 0000000000000000000000000000000000000000..93e0899c02040dfe35e4c9bbb47cf8290127bc8a
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/170.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>170.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\170.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>359</width>
+		<height>479</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>237</xmin>
+			<ymin>169</ymin>
+			<xmax>296</xmax>
+			<ymax>283</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/171.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/171.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/171.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/171.xml
new file mode 100644
index 0000000000000000000000000000000000000000..9fe9e59f7b1d484e8db47f98978f1a4c6dca804c
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/171.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>171.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\171.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>359</width>
+		<height>479</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>206</xmin>
+			<ymin>140</ymin>
+			<xmax>336</xmax>
+			<ymax>413</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/172.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/172.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/172.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/172.xml
new file mode 100644
index 0000000000000000000000000000000000000000..c50e9a089ae8591ba296db14db7561216445147b
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/172.xml	
@@ -0,0 +1,38 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>172.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\172.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>359</width>
+		<height>479</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>204</xmin>
+			<ymin>343</ymin>
+			<xmax>298</xmax>
+			<ymax>477</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>71</xmin>
+			<ymin>2</ymin>
+			<xmax>105</xmax>
+			<ymax>47</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/173.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/173.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/173.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/173.xml
new file mode 100644
index 0000000000000000000000000000000000000000..ee6b1c23b4ce4d5606f0fd6fcd31d829b23c22ec
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/173.xml	
@@ -0,0 +1,38 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>173.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\173.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>557</width>
+		<height>311</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>61</xmin>
+			<ymin>52</ymin>
+			<xmax>124</xmax>
+			<ymax>139</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>413</xmin>
+			<ymin>45</ymin>
+			<xmax>474</xmax>
+			<ymax>131</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/174.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/174.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/174.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/174.xml
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/174.xml	
@@ -0,0 +1,38 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>174.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\174.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>511</width>
+		<height>339</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>232</xmin>
+			<ymin>226</ymin>
+			<xmax>308</xmax>
+			<ymax>334</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>481</xmin>
+			<ymin>213</ymin>
+			<xmax>511</xmax>
+			<ymax>306</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/175.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/175.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/175.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/175.xml
new file mode 100644
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/175.xml	
@@ -0,0 +1,38 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>175.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\175.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>338</width>
+		<height>507</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>100</xmin>
+			<ymin>223</ymin>
+			<xmax>263</xmax>
+			<ymax>439</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>51</xmin>
+			<ymin>26</ymin>
+			<xmax>258</xmax>
+			<ymax>188</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/176.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/176.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/176.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/176.xml
new file mode 100644
index 0000000000000000000000000000000000000000..b5ff0834e0b3e5b442d87eae4106a92641ede84b
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/176.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>176.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\176.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>479</width>
+		<height>359</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>83</xmin>
+			<ymin>170</ymin>
+			<xmax>169</xmax>
+			<ymax>302</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/177.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/177.jpg
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new file mode 100644
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/177.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>177.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\177.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>1024</width>
+		<height>1024</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>360</xmin>
+			<ymin>314</ymin>
+			<xmax>649</xmax>
+			<ymax>734</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/178.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/178.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/178.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/178.xml
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/178.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>178.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\178.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>371</width>
+		<height>464</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>232</xmin>
+			<ymin>235</ymin>
+			<xmax>347</xmax>
+			<ymax>416</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/179.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/179.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/179.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/179.xml
new file mode 100644
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/179.xml	
@@ -0,0 +1,122 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>179.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\179.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>512</width>
+		<height>335</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>198</xmin>
+			<ymin>204</ymin>
+			<xmax>279</xmax>
+			<ymax>333</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>356</xmin>
+			<ymin>161</ymin>
+			<xmax>432</xmax>
+			<ymax>279</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>397</xmin>
+			<ymin>109</ymin>
+			<xmax>455</xmax>
+			<ymax>211</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>375</xmin>
+			<ymin>48</ymin>
+			<xmax>420</xmax>
+			<ymax>125</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>250</xmin>
+			<ymin>8</ymin>
+			<xmax>290</xmax>
+			<ymax>69</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>325</xmin>
+			<ymin>58</ymin>
+			<xmax>375</xmax>
+			<ymax>100</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>145</xmin>
+			<ymin>29</ymin>
+			<xmax>197</xmax>
+			<ymax>97</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>43</xmin>
+			<ymin>83</ymin>
+			<xmax>103</xmax>
+			<ymax>177</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>91</xmin>
+			<ymin>202</ymin>
+			<xmax>165</xmax>
+			<ymax>286</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/18.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/18.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/18.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/18.xml
new file mode 100644
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/18.xml	
@@ -0,0 +1,50 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>18.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\18.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>338</width>
+		<height>507</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>1</xmin>
+			<ymin>82</ymin>
+			<xmax>164</xmax>
+			<ymax>499</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>39</xmin>
+			<ymin>1</ymin>
+			<xmax>169</xmax>
+			<ymax>221</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>148</xmin>
+			<ymin>1</ymin>
+			<xmax>227</xmax>
+			<ymax>128</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/180.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/180.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/180.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/180.xml
new file mode 100644
index 0000000000000000000000000000000000000000..345f2e21caf998cfc9fe9f7767720238915be188
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/180.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>180.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\180.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>326</width>
+		<height>527</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>27</xmin>
+			<ymin>209</ymin>
+			<xmax>154</xmax>
+			<ymax>481</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/181.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/181.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/181.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/181.xml
new file mode 100644
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/181.xml	
@@ -0,0 +1,38 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>181.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\181.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>359</width>
+		<height>479</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>156</xmin>
+			<ymin>366</ymin>
+			<xmax>201</xmax>
+			<ymax>463</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>265</xmin>
+			<ymin>372</ymin>
+			<xmax>306</xmax>
+			<ymax>467</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/182.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/182.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/182.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/182.xml
new file mode 100644
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/182.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>182.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\182.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>359</width>
+		<height>479</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>175</xmin>
+			<ymin>42</ymin>
+			<xmax>292</xmax>
+			<ymax>277</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/183.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/183.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/183.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/183.xml
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/183.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>183.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\183.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>344</width>
+		<height>502</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>130</xmin>
+			<ymin>151</ymin>
+			<xmax>282</xmax>
+			<ymax>426</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/184.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/184.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/184.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/184.xml
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/184.xml	
@@ -0,0 +1,50 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>184.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\184.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>338</width>
+		<height>507</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>1</xmin>
+			<ymin>82</ymin>
+			<xmax>165</xmax>
+			<ymax>502</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>82</xmin>
+			<ymin>1</ymin>
+			<xmax>165</xmax>
+			<ymax>219</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>151</xmin>
+			<ymin>1</ymin>
+			<xmax>225</xmax>
+			<ymax>129</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/185.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/185.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/185.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/185.xml
new file mode 100644
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/185.xml	
@@ -0,0 +1,50 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>185.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\185.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>506</width>
+		<height>338</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>251</xmin>
+			<ymin>152</ymin>
+			<xmax>302</xmax>
+			<ymax>235</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>246</xmin>
+			<ymin>149</ymin>
+			<xmax>272</xmax>
+			<ymax>209</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>242</xmin>
+			<ymin>145</ymin>
+			<xmax>258</xmax>
+			<ymax>184</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/186.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/186.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/186.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/186.xml
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index 0000000000000000000000000000000000000000..1b9caedcfd4b05dd79f03205add860bcbbbb4794
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/186.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>186.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\186.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>338</width>
+		<height>507</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>154</xmin>
+			<ymin>205</ymin>
+			<xmax>284</xmax>
+			<ymax>423</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/187.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/187.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/187.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/187.xml
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/187.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>187.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\187.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>338</width>
+		<height>507</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>96</xmin>
+			<ymin>64</ymin>
+			<xmax>232</xmax>
+			<ymax>283</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/188.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/188.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/188.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/188.xml
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/188.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>188.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\188.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>350</width>
+		<height>490</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>60</xmin>
+			<ymin>36</ymin>
+			<xmax>291</xmax>
+			<ymax>457</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/189.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/189.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/189.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/189.xml
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/189.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>189.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\189.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>509</width>
+		<height>336</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>351</xmin>
+			<ymin>214</ymin>
+			<xmax>417</xmax>
+			<ymax>300</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/19.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/19.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/19.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/19.xml
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index 0000000000000000000000000000000000000000..9a03aee7b077abf3840bfca95d273e625ff36863
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/19.xml	
@@ -0,0 +1,62 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>19.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\19.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>507</width>
+		<height>338</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>73</xmin>
+			<ymin>48</ymin>
+			<xmax>258</xmax>
+			<ymax>314</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>168</xmin>
+			<ymin>59</ymin>
+			<xmax>232</xmax>
+			<ymax>189</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>201</xmin>
+			<ymin>61</ymin>
+			<xmax>251</xmax>
+			<ymax>147</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>224</xmin>
+			<ymin>63</ymin>
+			<xmax>255</xmax>
+			<ymax>116</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/190.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/190.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/190.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/190.xml
new file mode 100644
index 0000000000000000000000000000000000000000..cfb3760d0a0fce8a8be2782a0a593eef288e78f0
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/190.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>190.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\190.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>478</width>
+		<height>359</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>163</xmin>
+			<ymin>64</ymin>
+			<xmax>294</xmax>
+			<ymax>269</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/191.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/191.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/191.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/191.xml
new file mode 100644
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/191.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>191.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\191.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>478</width>
+		<height>359</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>255</xmin>
+			<ymin>205</ymin>
+			<xmax>314</xmax>
+			<ymax>330</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/192.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/192.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/192.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/192.xml
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/192.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>192.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\192.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>359</width>
+		<height>478</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>74</xmin>
+			<ymin>76</ymin>
+			<xmax>171</xmax>
+			<ymax>260</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/193.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/193.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/193.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/193.xml
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/193.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>193.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\193.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>338</width>
+		<height>507</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>116</xmin>
+			<ymin>52</ymin>
+			<xmax>210</xmax>
+			<ymax>276</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/194.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/194.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/194.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/194.xml
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/194.xml	
@@ -0,0 +1,50 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>194.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\194.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>359</width>
+		<height>479</height>
+		<depth>1</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>165</xmin>
+			<ymin>28</ymin>
+			<xmax>243</xmax>
+			<ymax>158</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>267</xmin>
+			<ymin>150</ymin>
+			<xmax>359</xmax>
+			<ymax>430</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>151</xmin>
+			<ymin>1</ymin>
+			<xmax>189</xmax>
+			<ymax>85</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/195.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/195.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/195.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/195.xml
new file mode 100644
index 0000000000000000000000000000000000000000..07914ca283422ac22138fe03f02c0644b962d834
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/195.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>195.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\195.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>507</width>
+		<height>338</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>133</xmin>
+			<ymin>102</ymin>
+			<xmax>235</xmax>
+			<ymax>257</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/196.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/196.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/196.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/196.xml
new file mode 100644
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/196.xml	
@@ -0,0 +1,38 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>196.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\196.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>507</width>
+		<height>338</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>74</xmin>
+			<ymin>115</ymin>
+			<xmax>177</xmax>
+			<ymax>315</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>213</xmin>
+			<ymin>130</ymin>
+			<xmax>256</xmax>
+			<ymax>196</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/197.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/197.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/197.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/197.xml
new file mode 100644
index 0000000000000000000000000000000000000000..88b9f33b48426890a95bccf97d48eab8f04303a9
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/197.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>197.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\197.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>338</width>
+		<height>507</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>184</xmin>
+			<ymin>63</ymin>
+			<xmax>289</xmax>
+			<ymax>256</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/198.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/198.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/198.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/198.xml
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/198.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>198.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\198.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>359</width>
+		<height>479</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>180</xmin>
+			<ymin>306</ymin>
+			<xmax>244</xmax>
+			<ymax>412</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/199.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/199.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/199.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/199.xml
new file mode 100644
index 0000000000000000000000000000000000000000..8803d31f786dd0ac7956a86029d9ae971d57930d
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/199.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>199.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\199.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>347</width>
+		<height>491</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>45</xmin>
+			<ymin>128</ymin>
+			<xmax>240</xmax>
+			<ymax>453</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/2.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/2.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/2.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/2.xml
new file mode 100644
index 0000000000000000000000000000000000000000..4f130d65a364588d87b526e4cb544e5206e50ec3
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/2.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>2.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\2.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>397</width>
+		<height>432</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>63</xmin>
+			<ymin>36</ymin>
+			<xmax>341</xmax>
+			<ymax>374</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/20.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/20.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/20.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/20.xml
new file mode 100644
index 0000000000000000000000000000000000000000..9cf2d551759cd629cfcc0fafd64c93a3cc67cfaa
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/20.xml	
@@ -0,0 +1,38 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>20.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\20.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>338</width>
+		<height>507</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>103</xmin>
+			<ymin>222</ymin>
+			<xmax>266</xmax>
+			<ymax>432</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>55</xmin>
+			<ymin>27</ymin>
+			<xmax>259</xmax>
+			<ymax>191</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/200.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/200.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/200.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/200.xml
new file mode 100644
index 0000000000000000000000000000000000000000..f03057c7db1ca714d0a170a4003286424a041eef
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/200.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>200.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\200.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>359</width>
+		<height>479</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>123</xmin>
+			<ymin>257</ymin>
+			<xmax>226</xmax>
+			<ymax>399</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/201.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/201.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/201.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/201.xml
new file mode 100644
index 0000000000000000000000000000000000000000..097e67470ec816cc3265961bd71ba480116908bc
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/201.xml	
@@ -0,0 +1,86 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>201.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\201.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>507</width>
+		<height>337</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>360</xmin>
+			<ymin>41</ymin>
+			<xmax>411</xmax>
+			<ymax>120</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>473</xmin>
+			<ymin>32</ymin>
+			<xmax>507</xmax>
+			<ymax>119</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>306</xmin>
+			<ymin>37</ymin>
+			<xmax>350</xmax>
+			<ymax>107</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>318</xmin>
+			<ymin>2</ymin>
+			<xmax>356</xmax>
+			<ymax>59</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>401</xmin>
+			<ymin>1</ymin>
+			<xmax>436</xmax>
+			<ymax>27</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>296</xmin>
+			<ymin>22</ymin>
+			<xmax>312</xmax>
+			<ymax>88</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/202.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/202.jpg
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/202.xml	
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+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>202.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\202.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>509</width>
+		<height>339</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
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+			<ymax>338</ymax>
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+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
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+	</object>
+</annotation>
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/203.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>203.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\203.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>553</width>
+		<height>312</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
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+	</object>
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/204.xml	
@@ -0,0 +1,266 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>204.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\204.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>509</width>
+		<height>339</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
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+		<difficult>0</difficult>
+		<bndbox>
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+			<ymax>295</ymax>
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+	</object>
+	<object>
+		<name>cone</name>
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+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
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+		<difficult>0</difficult>
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+			<ymax>292</ymax>
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+	</object>
+	<object>
+		<name>cone</name>
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+		<difficult>0</difficult>
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+			<xmin>127</xmin>
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+			<xmax>150</xmax>
+			<ymax>323</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
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+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>105</xmin>
+			<ymin>280</ymin>
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+			<ymax>321</ymax>
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+	</object>
+	<object>
+		<name>cone</name>
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+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>461</xmin>
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+			<xmax>479</xmax>
+			<ymax>288</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>433</xmin>
+			<ymin>254</ymin>
+			<xmax>449</xmax>
+			<ymax>280</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>412</xmin>
+			<ymin>253</ymin>
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+			<ymax>275</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>395</xmin>
+			<ymin>250</ymin>
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+			<ymax>271</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
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+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>371</xmin>
+			<ymin>247</ymin>
+			<xmax>381</xmax>
+			<ymax>265</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>494</xmin>
+			<ymin>250</ymin>
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+			<ymax>270</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>456</xmin>
+			<ymin>291</ymin>
+			<xmax>493</xmax>
+			<ymax>339</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>318</xmin>
+			<ymin>298</ymin>
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+			<ymax>339</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>170</xmin>
+			<ymin>296</ymin>
+			<xmax>190</xmax>
+			<ymax>339</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
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+			<ymax>281</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
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+			<ymax>262</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
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+			<xmin>295</xmin>
+			<ymin>254</ymin>
+			<xmax>302</xmax>
+			<ymax>273</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>151</xmin>
+			<ymin>272</ymin>
+			<xmax>169</xmax>
+			<ymax>306</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
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+		<bndbox>
+			<xmin>20</xmin>
+			<ymin>301</ymin>
+			<xmax>43</xmax>
+			<ymax>339</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>62</xmin>
+			<ymin>290</ymin>
+			<xmax>84</xmax>
+			<ymax>339</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
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+		<bndbox>
+			<xmin>365</xmin>
+			<ymin>297</ymin>
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+			<ymax>339</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/205.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/205.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/205.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/205.xml
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/205.xml	
@@ -0,0 +1,134 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>205.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\205.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>507</width>
+		<height>338</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>10</xmin>
+			<ymin>234</ymin>
+			<xmax>45</xmax>
+			<ymax>272</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>63</xmin>
+			<ymin>234</ymin>
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+			<ymax>271</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>114</xmin>
+			<ymin>234</ymin>
+			<xmax>146</xmax>
+			<ymax>269</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>165</xmin>
+			<ymin>234</ymin>
+			<xmax>195</xmax>
+			<ymax>268</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>216</xmin>
+			<ymin>231</ymin>
+			<xmax>245</xmax>
+			<ymax>267</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>264</xmin>
+			<ymin>233</ymin>
+			<xmax>296</xmax>
+			<ymax>268</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>316</xmin>
+			<ymin>232</ymin>
+			<xmax>347</xmax>
+			<ymax>268</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>367</xmin>
+			<ymin>230</ymin>
+			<xmax>399</xmax>
+			<ymax>268</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>419</xmin>
+			<ymin>230</ymin>
+			<xmax>451</xmax>
+			<ymax>265</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>469</xmin>
+			<ymin>231</ymin>
+			<xmax>504</xmax>
+			<ymax>265</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/206.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/206.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/206.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/206.xml
new file mode 100644
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/206.xml	
@@ -0,0 +1,38 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>206.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\206.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>338</width>
+		<height>507</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>104</xmin>
+			<ymin>218</ymin>
+			<xmax>263</xmax>
+			<ymax>435</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
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+			<ymin>30</ymin>
+			<xmax>259</xmax>
+			<ymax>190</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/207.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/207.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/207.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/207.xml
new file mode 100644
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/207.xml	
@@ -0,0 +1,62 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>207.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\207.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>510</width>
+		<height>339</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>252</xmin>
+			<ymin>195</ymin>
+			<xmax>299</xmax>
+			<ymax>277</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>387</xmin>
+			<ymin>186</ymin>
+			<xmax>433</xmax>
+			<ymax>260</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>449</xmin>
+			<ymin>183</ymin>
+			<xmax>492</xmax>
+			<ymax>250</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>227</xmin>
+			<ymin>218</ymin>
+			<xmax>247</xmax>
+			<ymax>271</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/208.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/208.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/208.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/208.xml
new file mode 100644
index 0000000000000000000000000000000000000000..eaea94d9dfeb48df25755c1f913666ae6693c2f4
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/208.xml	
@@ -0,0 +1,74 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>208.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\208.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>507</width>
+		<height>338</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>20</xmin>
+			<ymin>157</ymin>
+			<xmax>92</xmax>
+			<ymax>246</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>188</xmin>
+			<ymin>97</ymin>
+			<xmax>242</xmax>
+			<ymax>161</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>314</xmin>
+			<ymin>56</ymin>
+			<xmax>355</xmax>
+			<ymax>108</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>406</xmin>
+			<ymin>29</ymin>
+			<xmax>441</xmax>
+			<ymax>73</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>465</xmin>
+			<ymin>8</ymin>
+			<xmax>494</xmax>
+			<ymax>45</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/209.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/209.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/209.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/209.xml
new file mode 100644
index 0000000000000000000000000000000000000000..7ec9a42458eb7b3406ae89335df6d0cad6c4a601
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/209.xml	
@@ -0,0 +1,74 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>209.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\209.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>341</width>
+		<height>502</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>1</xmin>
+			<ymin>245</ymin>
+			<xmax>124</xmax>
+			<ymax>430</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>115</xmin>
+			<ymin>102</ymin>
+			<xmax>210</xmax>
+			<ymax>203</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>175</xmin>
+			<ymin>41</ymin>
+			<xmax>232</xmax>
+			<ymax>110</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>231</xmin>
+			<ymin>15</ymin>
+			<xmax>278</xmax>
+			<ymax>74</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>267</xmin>
+			<ymin>1</ymin>
+			<xmax>309</xmax>
+			<ymax>39</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/21.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/21.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/21.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/21.xml
new file mode 100644
index 0000000000000000000000000000000000000000..ecca5bab9afbe365f9f650d7884c5fcee8ec5f4e
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/21.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>21.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\21.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>337</width>
+		<height>508</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>14</xmin>
+			<ymin>9</ymin>
+			<xmax>320</xmax>
+			<ymax>477</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/210.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/210.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/210.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/210.xml
new file mode 100644
index 0000000000000000000000000000000000000000..43c7ce58b63137a2404f720ee833d879d0e14690
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/210.xml	
@@ -0,0 +1,50 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>210.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\210.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>359</width>
+		<height>479</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>109</xmin>
+			<ymin>68</ymin>
+			<xmax>169</xmax>
+			<ymax>174</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>215</xmin>
+			<ymin>181</ymin>
+			<xmax>340</xmax>
+			<ymax>302</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>135</xmin>
+			<ymin>141</ymin>
+			<xmax>224</xmax>
+			<ymax>244</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/211.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/211.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/211.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/211.xml
new file mode 100644
index 0000000000000000000000000000000000000000..a68e768a75174393828432202b8f90095f5ef2e1
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/211.xml	
@@ -0,0 +1,86 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>211.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\211.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>507</width>
+		<height>338</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>169</xmin>
+			<ymin>54</ymin>
+			<xmax>228</xmax>
+			<ymax>131</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>109</xmin>
+			<ymin>51</ymin>
+			<xmax>172</xmax>
+			<ymax>134</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>26</xmin>
+			<ymin>52</ymin>
+			<xmax>91</xmax>
+			<ymax>131</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>280</xmin>
+			<ymin>51</ymin>
+			<xmax>334</xmax>
+			<ymax>127</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>351</xmin>
+			<ymin>52</ymin>
+			<xmax>398</xmax>
+			<ymax>126</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>404</xmin>
+			<ymin>50</ymin>
+			<xmax>452</xmax>
+			<ymax>128</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/212.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/212.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/212.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/212.xml
new file mode 100644
index 0000000000000000000000000000000000000000..0fd6509eec8077a0567167eab9795c40e310e932
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/212.xml	
@@ -0,0 +1,98 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>212.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\212.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>507</width>
+		<height>338</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>49</xmin>
+			<ymin>218</ymin>
+			<xmax>81</xmax>
+			<ymax>262</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>146</xmin>
+			<ymin>218</ymin>
+			<xmax>174</xmax>
+			<ymax>260</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>182</xmin>
+			<ymin>218</ymin>
+			<xmax>210</xmax>
+			<ymax>259</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>210</xmin>
+			<ymin>220</ymin>
+			<xmax>230</xmax>
+			<ymax>257</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>230</xmin>
+			<ymin>218</ymin>
+			<xmax>253</xmax>
+			<ymax>256</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>301</xmin>
+			<ymin>218</ymin>
+			<xmax>326</xmax>
+			<ymax>255</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>443</xmin>
+			<ymin>217</ymin>
+			<xmax>474</xmax>
+			<ymax>250</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/213.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/213.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/213.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/213.xml
new file mode 100644
index 0000000000000000000000000000000000000000..f8f6777cbe07ce75ecc9d15f7ba2e8d3956e1d14
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/213.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>213.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\213.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>509</width>
+		<height>338</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>209</xmin>
+			<ymin>97</ymin>
+			<xmax>319</xmax>
+			<ymax>304</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/214.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/214.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/214.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/214.xml
new file mode 100644
index 0000000000000000000000000000000000000000..b866d7032fc7e4b6c4caba3501ad1c6a49a0552f
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/214.xml	
@@ -0,0 +1,62 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>214.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\214.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>339</width>
+		<height>508</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>52</xmin>
+			<ymin>388</ymin>
+			<xmax>93</xmax>
+			<ymax>495</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>102</xmin>
+			<ymin>365</ymin>
+			<xmax>181</xmax>
+			<ymax>496</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>191</xmin>
+			<ymin>370</ymin>
+			<xmax>248</xmax>
+			<ymax>498</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>240</xmin>
+			<ymin>381</ymin>
+			<xmax>307</xmax>
+			<ymax>501</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/215.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/215.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/215.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/215.xml
new file mode 100644
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/215.xml	
@@ -0,0 +1,50 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>215.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\215.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>416</width>
+		<height>416</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>104</xmin>
+			<ymin>319</ymin>
+			<xmax>141</xmax>
+			<ymax>380</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>210</xmin>
+			<ymin>321</ymin>
+			<xmax>246</xmax>
+			<ymax>382</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>294</xmin>
+			<ymin>323</ymin>
+			<xmax>329</xmax>
+			<ymax>381</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/216.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/216.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/216.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/216.xml
new file mode 100644
index 0000000000000000000000000000000000000000..68cf18f7ed0afd5dee47d91b1741bc995d7af7f1
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/216.xml	
@@ -0,0 +1,50 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>216.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\216.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>509</width>
+		<height>338</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>143</xmin>
+			<ymin>114</ymin>
+			<xmax>234</xmax>
+			<ymax>273</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>217</xmin>
+			<ymin>152</ymin>
+			<xmax>269</xmax>
+			<ymax>248</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>1</xmin>
+			<ymin>1</ymin>
+			<xmax>114</xmax>
+			<ymax>336</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/217.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/217.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/217.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/217.xml
new file mode 100644
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/217.xml	
@@ -0,0 +1,50 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>217.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\217.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>341</width>
+		<height>506</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>1</xmin>
+			<ymin>219</ymin>
+			<xmax>142</xmax>
+			<ymax>500</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>238</xmin>
+			<ymin>240</ymin>
+			<xmax>341</xmax>
+			<ymax>439</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>304</xmin>
+			<ymin>283</ymin>
+			<xmax>341</xmax>
+			<ymax>391</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/218.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/218.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/218.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/218.xml
new file mode 100644
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/218.xml	
@@ -0,0 +1,86 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>218.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\218.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>508</width>
+		<height>338</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>1</xmin>
+			<ymin>34</ymin>
+			<xmax>132</xmax>
+			<ymax>338</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>140</xmin>
+			<ymin>24</ymin>
+			<xmax>246</xmax>
+			<ymax>262</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>244</xmin>
+			<ymin>22</ymin>
+			<xmax>320</xmax>
+			<ymax>214</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>326</xmin>
+			<ymin>15</ymin>
+			<xmax>385</xmax>
+			<ymax>153</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>392</xmin>
+			<ymin>4</ymin>
+			<xmax>420</xmax>
+			<ymax>97</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>376</xmin>
+			<ymin>7</ymin>
+			<xmax>394</xmax>
+			<ymax>126</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/219.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/219.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/219.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/219.xml
new file mode 100644
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/219.xml	
@@ -0,0 +1,194 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>219.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\219.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>510</width>
+		<height>337</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>308</xmin>
+			<ymin>168</ymin>
+			<xmax>402</xmax>
+			<ymax>327</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>297</xmin>
+			<ymin>120</ymin>
+			<xmax>360</xmax>
+			<ymax>250</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>279</xmin>
+			<ymin>92</ymin>
+			<xmax>326</xmax>
+			<ymax>207</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>369</xmin>
+			<ymin>24</ymin>
+			<xmax>398</xmax>
+			<ymax>77</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>432</xmin>
+			<ymin>20</ymin>
+			<xmax>459</xmax>
+			<ymax>75</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>313</xmin>
+			<ymin>21</ymin>
+			<xmax>341</xmax>
+			<ymax>76</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>152</xmin>
+			<ymin>16</ymin>
+			<xmax>184</xmax>
+			<ymax>64</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>121</xmin>
+			<ymin>15</ymin>
+			<xmax>153</xmax>
+			<ymax>61</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>103</xmin>
+			<ymin>15</ymin>
+			<xmax>121</xmax>
+			<ymax>65</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>72</xmin>
+			<ymin>16</ymin>
+			<xmax>102</xmax>
+			<ymax>62</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>39</xmin>
+			<ymin>17</ymin>
+			<xmax>75</xmax>
+			<ymax>67</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>15</xmin>
+			<ymin>17</ymin>
+			<xmax>51</xmax>
+			<ymax>69</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>260</xmin>
+			<ymin>74</ymin>
+			<xmax>311</xmax>
+			<ymax>173</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>252</xmin>
+			<ymin>61</ymin>
+			<xmax>291</xmax>
+			<ymax>148</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>223</xmin>
+			<ymin>30</ymin>
+			<xmax>244</xmax>
+			<ymax>87</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/22.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/22.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/22.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/22.xml
new file mode 100644
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/22.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>22.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\22.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>337</width>
+		<height>508</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>3</xmin>
+			<ymin>37</ymin>
+			<xmax>333</xmax>
+			<ymax>478</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/220.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/220.jpg
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/220.xml	
@@ -0,0 +1,74 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>220.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\220.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>505</width>
+		<height>342</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>202</xmin>
+			<ymin>92</ymin>
+			<xmax>249</xmax>
+			<ymax>178</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
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+		<bndbox>
+			<xmin>225</xmin>
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+			<ymax>321</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
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+		<difficult>0</difficult>
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+			<xmin>323</xmin>
+			<ymin>111</ymin>
+			<xmax>370</xmax>
+			<ymax>167</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>265</xmin>
+			<ymin>94</ymin>
+			<xmax>286</xmax>
+			<ymax>138</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>230</xmin>
+			<ymin>109</ymin>
+			<xmax>247</xmax>
+			<ymax>134</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/221.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/221.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/221.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/221.xml
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/221.xml	
@@ -0,0 +1,170 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>221.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\221.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>507</width>
+		<height>338</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
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+		<difficult>0</difficult>
+		<bndbox>
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+			<ymin>196</ymin>
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+			<ymax>241</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
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+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>118</xmin>
+			<ymin>197</ymin>
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+			<ymax>241</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>145</xmin>
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+			<xmax>174</xmax>
+			<ymax>243</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>174</xmin>
+			<ymin>197</ymin>
+			<xmax>201</xmax>
+			<ymax>243</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>200</xmin>
+			<ymin>199</ymin>
+			<xmax>227</xmax>
+			<ymax>244</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>227</xmin>
+			<ymin>198</ymin>
+			<xmax>254</xmax>
+			<ymax>242</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>255</xmin>
+			<ymin>197</ymin>
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+			<ymax>243</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>282</xmin>
+			<ymin>198</ymin>
+			<xmax>308</xmax>
+			<ymax>243</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>309</xmin>
+			<ymin>198</ymin>
+			<xmax>337</xmax>
+			<ymax>244</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>338</xmin>
+			<ymin>196</ymin>
+			<xmax>362</xmax>
+			<ymax>247</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>362</xmin>
+			<ymin>197</ymin>
+			<xmax>391</xmax>
+			<ymax>246</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>392</xmin>
+			<ymin>198</ymin>
+			<xmax>417</xmax>
+			<ymax>244</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>417</xmin>
+			<ymin>195</ymin>
+			<xmax>445</xmax>
+			<ymax>244</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/222.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/222.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/222.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/222.xml
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/222.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>222.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\222.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>339</width>
+		<height>509</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>1</xmin>
+			<ymin>140</ymin>
+			<xmax>179</xmax>
+			<ymax>497</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/223.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/223.jpg
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/223.xml	
@@ -0,0 +1,38 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>223.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\223.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>507</width>
+		<height>338</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>99</xmin>
+			<ymin>60</ymin>
+			<xmax>194</xmax>
+			<ymax>234</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>279</xmin>
+			<ymin>51</ymin>
+			<xmax>398</xmax>
+			<ymax>303</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/224.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/224.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/224.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/224.xml
new file mode 100644
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/224.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>224.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\224.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>338</width>
+		<height>507</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>135</xmin>
+			<ymin>182</ymin>
+			<xmax>266</xmax>
+			<ymax>462</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/225.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/225.jpg
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/225.xml	
@@ -0,0 +1,38 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>225.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\225.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>479</width>
+		<height>359</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>180</xmin>
+			<ymin>139</ymin>
+			<xmax>236</xmax>
+			<ymax>234</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>252</xmin>
+			<ymin>126</ymin>
+			<xmax>304</xmax>
+			<ymax>213</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/226.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/226.jpg
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new file mode 100644
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/226.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>226.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\226.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>678</width>
+		<height>1024</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>123</xmin>
+			<ymin>145</ymin>
+			<xmax>573</xmax>
+			<ymax>970</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/227.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/227.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/227.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/227.xml
new file mode 100644
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/227.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>227.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\227.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>338</width>
+		<height>511</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>16</xmin>
+			<ymin>18</ymin>
+			<xmax>280</xmax>
+			<ymax>499</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/228.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/228.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/228.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/228.xml
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/228.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>228.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\228.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>170</width>
+		<height>170</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>85</xmin>
+			<ymin>16</ymin>
+			<xmax>159</xmax>
+			<ymax>147</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/229.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/229.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/229.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/229.xml
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index 0000000000000000000000000000000000000000..944b54f47c74890477224450f1aff7127ca3b421
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/229.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>229.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\229.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>416</width>
+		<height>416</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>214</xmin>
+			<ymin>38</ymin>
+			<xmax>387</xmax>
+			<ymax>363</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/23.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/23.jpg
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new file mode 100644
index 0000000000000000000000000000000000000000..85ba55bbaf08d5df33644dc84a711c8e5b1b2a07
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/23.xml	
@@ -0,0 +1,38 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>23.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\23.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>478</width>
+		<height>359</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>16</xmin>
+			<ymin>29</ymin>
+			<xmax>261</xmax>
+			<ymax>310</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>242</xmin>
+			<ymin>76</ymin>
+			<xmax>439</xmax>
+			<ymax>271</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/230.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/230.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/230.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/230.xml
new file mode 100644
index 0000000000000000000000000000000000000000..d056eb59e926bbd8f90485ee74a71c63d09617d8
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/230.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>230.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\230.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>356</width>
+		<height>481</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>101</xmin>
+			<ymin>88</ymin>
+			<xmax>244</xmax>
+			<ymax>318</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/231.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/231.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/231.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/231.xml
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/231.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>231.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\231.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>338</width>
+		<height>511</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>97</xmin>
+			<ymin>87</ymin>
+			<xmax>234</xmax>
+			<ymax>335</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/232.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/232.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/232.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/232.xml
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/232.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>232.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\232.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>416</width>
+		<height>416</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>24</xmin>
+			<ymin>51</ymin>
+			<xmax>223</xmax>
+			<ymax>387</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/233.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/233.jpg
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/233.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>233.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\233.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>482</width>
+		<height>359</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>375</xmin>
+			<ymin>1</ymin>
+			<xmax>479</xmax>
+			<ymax>210</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/234.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/234.jpg
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new file mode 100644
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/234.xml	
@@ -0,0 +1,38 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>234.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\234.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>371</width>
+		<height>464</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>6</xmin>
+			<ymin>155</ymin>
+			<xmax>173</xmax>
+			<ymax>456</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>71</xmin>
+			<ymin>23</ymin>
+			<xmax>224</xmax>
+			<ymax>371</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/235.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/235.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/235.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/235.xml
new file mode 100644
index 0000000000000000000000000000000000000000..39f8da132e732cd8868bfb01c3a222fafeb16cbf
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/235.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>235.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\235.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>509</width>
+		<height>339</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>271</xmin>
+			<ymin>42</ymin>
+			<xmax>386</xmax>
+			<ymax>206</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/236.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/236.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/236.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/236.xml
new file mode 100644
index 0000000000000000000000000000000000000000..26ba8bc91a84826611b19c7018710a94c2127497
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/236.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>236.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\236.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>384</width>
+		<height>450</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>25</xmin>
+			<ymin>80</ymin>
+			<xmax>225</xmax>
+			<ymax>418</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/237.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/237.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/237.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/237.xml
new file mode 100644
index 0000000000000000000000000000000000000000..add17552a010f76d44ca23368e5bf3e25e92b78a
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/237.xml	
@@ -0,0 +1,62 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>237.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\237.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>357</width>
+		<height>479</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>39</xmin>
+			<ymin>317</ymin>
+			<xmax>122</xmax>
+			<ymax>427</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>30</xmin>
+			<ymin>288</ymin>
+			<xmax>72</xmax>
+			<ymax>361</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>17</xmin>
+			<ymin>278</ymin>
+			<xmax>46</xmax>
+			<ymax>332</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>7</xmin>
+			<ymin>266</ymin>
+			<xmax>26</xmax>
+			<ymax>298</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/238.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/238.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/238.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/238.xml
new file mode 100644
index 0000000000000000000000000000000000000000..093e95bfbe254312e2fdbda602fd8b7fbdea7c92
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/238.xml	
@@ -0,0 +1,74 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>238.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\238.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>478</width>
+		<height>359</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>80</xmin>
+			<ymin>35</ymin>
+			<xmax>182</xmax>
+			<ymax>209</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>152</xmin>
+			<ymin>14</ymin>
+			<xmax>238</xmax>
+			<ymax>161</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>154</xmin>
+			<ymin>84</ymin>
+			<xmax>259</xmax>
+			<ymax>281</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>269</xmin>
+			<ymin>72</ymin>
+			<xmax>360</xmax>
+			<ymax>256</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>249</xmin>
+			<ymin>26</ymin>
+			<xmax>320</xmax>
+			<ymax>174</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/239.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/239.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/239.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/239.xml
new file mode 100644
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/239.xml	
@@ -0,0 +1,38 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>239.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\239.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>509</width>
+		<height>339</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>172</xmin>
+			<ymin>54</ymin>
+			<xmax>225</xmax>
+			<ymax>147</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>340</xmin>
+			<ymin>53</ymin>
+			<xmax>479</xmax>
+			<ymax>326</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/24.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/24.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/24.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/24.xml
new file mode 100644
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/24.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>24.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\24.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>339</width>
+		<height>509</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>34</xmin>
+			<ymin>65</ymin>
+			<xmax>319</xmax>
+			<ymax>474</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/240.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/240.jpg
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/240.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>240.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\240.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>507</width>
+		<height>339</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>117</xmin>
+			<ymin>71</ymin>
+			<xmax>187</xmax>
+			<ymax>185</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/241.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/241.jpg
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/241.xml	
@@ -0,0 +1,38 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>241.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\241.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>509</width>
+		<height>339</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>75</xmin>
+			<ymin>44</ymin>
+			<xmax>138</xmax>
+			<ymax>133</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>279</xmin>
+			<ymin>162</ymin>
+			<xmax>357</xmax>
+			<ymax>282</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/242.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/242.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/242.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/242.xml
new file mode 100644
index 0000000000000000000000000000000000000000..7c184931388544e153ce3c688a22bdc2ad1a65c1
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/242.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>242.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\242.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>576</width>
+		<height>1024</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>207</xmin>
+			<ymin>34</ymin>
+			<xmax>552</xmax>
+			<ymax>668</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/243.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/243.jpg
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/243.xml	
@@ -0,0 +1,74 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>243.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\243.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>507</width>
+		<height>338</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>209</xmin>
+			<ymin>30</ymin>
+			<xmax>315</xmax>
+			<ymax>226</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>25</xmin>
+			<ymin>1</ymin>
+			<xmax>113</xmax>
+			<ymax>125</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>83</xmin>
+			<ymin>1</ymin>
+			<xmax>163</xmax>
+			<ymax>81</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>262</xmin>
+			<ymin>114</ymin>
+			<xmax>410</xmax>
+			<ymax>338</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>270</xmin>
+			<ymin>1</ymin>
+			<xmax>332</xmax>
+			<ymax>124</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/244.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/244.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/244.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/244.xml
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/244.xml	
@@ -0,0 +1,122 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>244.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\244.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>338</width>
+		<height>507</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>272</xmin>
+			<ymin>163</ymin>
+			<xmax>338</xmax>
+			<ymax>272</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>212</xmin>
+			<ymin>175</ymin>
+			<xmax>271</xmax>
+			<ymax>264</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>169</xmin>
+			<ymin>181</ymin>
+			<xmax>223</xmax>
+			<ymax>259</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>137</xmin>
+			<ymin>189</ymin>
+			<xmax>183</xmax>
+			<ymax>258</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>115</xmin>
+			<ymin>194</ymin>
+			<xmax>145</xmax>
+			<ymax>256</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>94</xmin>
+			<ymin>198</ymin>
+			<xmax>124</xmax>
+			<ymax>253</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>80</xmin>
+			<ymin>200</ymin>
+			<xmax>104</xmax>
+			<ymax>252</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>68</xmin>
+			<ymin>203</ymin>
+			<xmax>89</xmax>
+			<ymax>250</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>58</xmin>
+			<ymin>206</ymin>
+			<xmax>73</xmax>
+			<ymax>247</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/245.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/245.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/245.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/245.xml
new file mode 100644
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/245.xml	
@@ -0,0 +1,98 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>245.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\245.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>338</width>
+		<height>507</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>1</xmin>
+			<ymin>275</ymin>
+			<xmax>68</xmax>
+			<ymax>362</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>72</xmin>
+			<ymin>286</ymin>
+			<xmax>124</xmax>
+			<ymax>358</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>123</xmin>
+			<ymin>294</ymin>
+			<xmax>164</xmax>
+			<ymax>354</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>159</xmin>
+			<ymin>299</ymin>
+			<xmax>193</xmax>
+			<ymax>354</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>190</xmin>
+			<ymin>302</ymin>
+			<xmax>215</xmax>
+			<ymax>354</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>208</xmin>
+			<ymin>306</ymin>
+			<xmax>232</xmax>
+			<ymax>354</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>227</xmin>
+			<ymin>312</ymin>
+			<xmax>249</xmax>
+			<ymax>350</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/246.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/246.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/246.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/246.xml
new file mode 100644
index 0000000000000000000000000000000000000000..f9cca2da0d6f09bbe1e46a9fdea5d086b8d00fa9
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/246.xml	
@@ -0,0 +1,86 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>246.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\246.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>338</width>
+		<height>507</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>1</xmin>
+			<ymin>199</ymin>
+			<xmax>111</xmax>
+			<ymax>353</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>87</xmin>
+			<ymin>238</ymin>
+			<xmax>158</xmax>
+			<ymax>346</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>138</xmin>
+			<ymin>252</ymin>
+			<xmax>184</xmax>
+			<ymax>340</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>162</xmin>
+			<ymin>267</ymin>
+			<xmax>204</xmax>
+			<ymax>338</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>187</xmin>
+			<ymin>275</ymin>
+			<xmax>211</xmax>
+			<ymax>337</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>210</xmin>
+			<ymin>287</ymin>
+			<xmax>225</xmax>
+			<ymax>335</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/247.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/247.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/247.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/247.xml
new file mode 100644
index 0000000000000000000000000000000000000000..151d36129f29ad732edc32b3a72394f32b0d8a39
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/247.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>247.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\247.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>509</width>
+		<height>339</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>197</xmin>
+			<ymin>101</ymin>
+			<xmax>344</xmax>
+			<ymax>293</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/248.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/248.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/248.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/248.xml
new file mode 100644
index 0000000000000000000000000000000000000000..8a568d3149339cd72a913968fbd12a3c19f04568
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/248.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>248.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\248.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>509</width>
+		<height>339</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>296</xmin>
+			<ymin>184</ymin>
+			<xmax>408</xmax>
+			<ymax>319</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/249.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/249.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/249.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/249.xml
new file mode 100644
index 0000000000000000000000000000000000000000..a48aa2ca13482356050272461e63b309381ed7a8
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/249.xml	
@@ -0,0 +1,134 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>249.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\249.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>507</width>
+		<height>338</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>130</xmin>
+			<ymin>259</ymin>
+			<xmax>171</xmax>
+			<ymax>321</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>165</xmin>
+			<ymin>232</ymin>
+			<xmax>199</xmax>
+			<ymax>282</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>189</xmin>
+			<ymin>213</ymin>
+			<xmax>216</xmax>
+			<ymax>255</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>206</xmin>
+			<ymin>200</ymin>
+			<xmax>230</xmax>
+			<ymax>237</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>218</xmin>
+			<ymin>190</ymin>
+			<xmax>239</xmax>
+			<ymax>221</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>230</xmin>
+			<ymin>183</ymin>
+			<xmax>246</xmax>
+			<ymax>211</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>237</xmin>
+			<ymin>178</ymin>
+			<xmax>254</xmax>
+			<ymax>201</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>243</xmin>
+			<ymin>172</ymin>
+			<xmax>259</xmax>
+			<ymax>193</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>256</xmin>
+			<ymin>162</ymin>
+			<xmax>266</xmax>
+			<ymax>181</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>85</xmin>
+			<ymin>306</ymin>
+			<xmax>113</xmax>
+			<ymax>338</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/25.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/25.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/25.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/25.xml
new file mode 100644
index 0000000000000000000000000000000000000000..ff99b86d07e94aa8ae0bb50c3c99074a2773dbc1
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/25.xml	
@@ -0,0 +1,38 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>25.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\25.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>1024</width>
+		<height>989</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>106</xmin>
+			<ymin>107</ymin>
+			<xmax>640</xmax>
+			<ymax>913</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>470</xmin>
+			<ymin>281</ymin>
+			<xmax>936</xmax>
+			<ymax>657</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/250.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/250.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/250.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/250.xml
new file mode 100644
index 0000000000000000000000000000000000000000..4db7a2b25d00279a3df16b09a64b8228049699c5
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/250.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>250.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\250.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>533</width>
+		<height>651</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>172</xmin>
+			<ymin>201</ymin>
+			<xmax>457</xmax>
+			<ymax>594</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/251.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/251.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/251.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/251.xml
new file mode 100644
index 0000000000000000000000000000000000000000..7fcc8409c4e90d8ad740eb6ce5fa1cdac781c6a1
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/251.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>251.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\251.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>353</width>
+		<height>485</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>56</xmin>
+			<ymin>3</ymin>
+			<xmax>292</xmax>
+			<ymax>445</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/252.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/252.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/252.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/252.xml
new file mode 100644
index 0000000000000000000000000000000000000000..dadb13d259b16ff2a085bd66396e33e467727e9b
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/252.xml	
@@ -0,0 +1,122 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>252.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\252.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>375</width>
+		<height>458</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>181</xmin>
+			<ymin>298</ymin>
+			<xmax>231</xmax>
+			<ymax>389</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>205</xmin>
+			<ymin>242</ymin>
+			<xmax>243</xmax>
+			<ymax>316</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>247</xmin>
+			<ymin>205</ymin>
+			<xmax>279</xmax>
+			<ymax>266</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>258</xmin>
+			<ymin>172</ymin>
+			<xmax>286</xmax>
+			<ymax>219</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>244</xmin>
+			<ymin>147</ymin>
+			<xmax>265</xmax>
+			<ymax>182</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>198</xmin>
+			<ymin>128</ymin>
+			<xmax>222</xmax>
+			<ymax>158</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>145</xmin>
+			<ymin>129</ymin>
+			<xmax>162</xmax>
+			<ymax>162</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>100</xmin>
+			<ymin>152</ymin>
+			<xmax>123</xmax>
+			<ymax>194</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>84</xmin>
+			<ymin>180</ymin>
+			<xmax>112</xmax>
+			<ymax>234</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/253.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/253.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/253.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/253.xml
new file mode 100644
index 0000000000000000000000000000000000000000..16c933e2bf71d555361d697b4d5ce3cdb81222c8
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/253.xml	
@@ -0,0 +1,38 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>253.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\253.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>338</width>
+		<height>507</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>150</xmin>
+			<ymin>218</ymin>
+			<xmax>262</xmax>
+			<ymax>464</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>273</xmin>
+			<ymin>243</ymin>
+			<xmax>338</xmax>
+			<ymax>507</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/254.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/254.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/254.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/254.xml
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/254.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>254.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\254.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>478</width>
+		<height>358</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>29</xmin>
+			<ymin>108</ymin>
+			<xmax>423</xmax>
+			<ymax>309</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/255.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/255.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/255.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/255.xml
new file mode 100644
index 0000000000000000000000000000000000000000..e39b53d456ba8157efca896ce47c63537b17c836
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/255.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>255.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\255.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>339</width>
+		<height>506</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>68</xmin>
+			<ymin>52</ymin>
+			<xmax>299</xmax>
+			<ymax>467</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/26.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/26.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/26.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/26.xml
new file mode 100644
index 0000000000000000000000000000000000000000..21e4685b0b0c520c8a9b3c399189003e0b003557
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/26.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>26.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\26.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>686</width>
+		<height>1024</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>120</xmin>
+			<ymin>115</ymin>
+			<xmax>555</xmax>
+			<ymax>963</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/27.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/27.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/27.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/27.xml
new file mode 100644
index 0000000000000000000000000000000000000000..269536d45d00144d322bc0772181ce30b54d0ebc
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/27.xml	
@@ -0,0 +1,86 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>27.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\27.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>602</width>
+		<height>1024</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>177</xmin>
+			<ymin>125</ymin>
+			<xmax>421</xmax>
+			<ymax>697</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>337</xmin>
+			<ymin>269</ymin>
+			<xmax>602</xmax>
+			<ymax>1023</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>168</xmin>
+			<ymin>105</ymin>
+			<xmax>316</xmax>
+			<ymax>464</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>166</xmin>
+			<ymin>115</ymin>
+			<xmax>247</xmax>
+			<ymax>343</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>48</xmin>
+			<ymin>74</ymin>
+			<xmax>124</xmax>
+			<ymax>207</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>1</xmin>
+			<ymin>73</ymin>
+			<xmax>45</xmax>
+			<ymax>179</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/28.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/28.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/28.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/28.xml
new file mode 100644
index 0000000000000000000000000000000000000000..cbd1807eb72ba55ed8fdca85ea4c3ac21ee60edc
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/28.xml	
@@ -0,0 +1,86 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>28.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\28.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>1024</width>
+		<height>658</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>1</xmin>
+			<ymin>46</ymin>
+			<xmax>246</xmax>
+			<ymax>655</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>1</xmin>
+			<ymin>42</ymin>
+			<xmax>265</xmax>
+			<ymax>562</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>431</xmin>
+			<ymin>225</ymin>
+			<xmax>537</xmax>
+			<ymax>393</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>652</xmin>
+			<ymin>244</ymin>
+			<xmax>737</xmax>
+			<ymax>371</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>813</xmin>
+			<ymin>260</ymin>
+			<xmax>894</xmax>
+			<ymax>362</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>316</xmin>
+			<ymin>197</ymin>
+			<xmax>389</xmax>
+			<ymax>432</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/29.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/29.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/29.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/29.xml
new file mode 100644
index 0000000000000000000000000000000000000000..a87ff5cd2153626ebc41d319f3c832a7b08269ee
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/29.xml	
@@ -0,0 +1,74 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>29.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\29.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>1024</width>
+		<height>658</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>390</xmin>
+			<ymin>259</ymin>
+			<xmax>632</xmax>
+			<ymax>417</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>806</xmin>
+			<ymin>256</ymin>
+			<xmax>895</xmax>
+			<ymax>361</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>1</xmin>
+			<ymin>41</ymin>
+			<xmax>261</xmax>
+			<ymax>561</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>250</xmin>
+			<ymin>181</ymin>
+			<xmax>393</xmax>
+			<ymax>430</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>711</xmin>
+			<ymin>251</ymin>
+			<xmax>788</xmax>
+			<ymax>365</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/3.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/3.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/3.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/3.xml
new file mode 100644
index 0000000000000000000000000000000000000000..4c93fbceab56b63b9c500003be55dcd5179ce1ce
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/3.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>3.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\3.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>142</width>
+		<height>170</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>5</xmin>
+			<ymin>10</ymin>
+			<xmax>137</xmax>
+			<ymax>162</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/30.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/30.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/30.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/30.xml
new file mode 100644
index 0000000000000000000000000000000000000000..7ad1e12f8c1589093df8009ac398dcbc0c9eb609
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/30.xml	
@@ -0,0 +1,62 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>30.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\30.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>1024</width>
+		<height>768</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>1</xmin>
+			<ymin>1</ymin>
+			<xmax>297</xmax>
+			<ymax>756</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>48</xmin>
+			<ymin>1</ymin>
+			<xmax>352</xmax>
+			<ymax>523</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>183</xmin>
+			<ymin>48</ymin>
+			<xmax>384</xmax>
+			<ymax>418</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>299</xmin>
+			<ymin>125</ymin>
+			<xmax>589</xmax>
+			<ymax>360</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/31.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/31.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/31.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/31.xml
new file mode 100644
index 0000000000000000000000000000000000000000..95ea8048e389114c90ad1f35dd058f2bd85358e1
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/31.xml	
@@ -0,0 +1,38 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>31.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\31.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>478</width>
+		<height>359</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>19</xmin>
+			<ymin>32</ymin>
+			<xmax>261</xmax>
+			<ymax>309</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>268</xmin>
+			<ymin>76</ymin>
+			<xmax>438</xmax>
+			<ymax>275</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/32.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/32.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/32.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/32.xml
new file mode 100644
index 0000000000000000000000000000000000000000..0f671931ef4e20b4ce89001337764f5eedb11c20
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/32.xml	
@@ -0,0 +1,74 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>32.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\32.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>1024</width>
+		<height>768</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>105</xmin>
+			<ymin>338</ymin>
+			<xmax>289</xmax>
+			<ymax>547</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>229</xmin>
+			<ymin>393</ymin>
+			<xmax>473</xmax>
+			<ymax>594</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>538</xmin>
+			<ymin>395</ymin>
+			<xmax>737</xmax>
+			<ymax>650</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>413</xmin>
+			<ymin>347</ymin>
+			<xmax>566</xmax>
+			<ymax>498</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>650</xmin>
+			<ymin>341</ymin>
+			<xmax>800</xmax>
+			<ymax>466</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/33.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/33.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/33.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/33.xml
new file mode 100644
index 0000000000000000000000000000000000000000..b18fd0d3c5481f088179262b8fbef9fd89a41aec
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/33.xml	
@@ -0,0 +1,62 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>33.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\33.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>507</width>
+		<height>338</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>83</xmin>
+			<ymin>49</ymin>
+			<xmax>258</xmax>
+			<ymax>314</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>181</xmin>
+			<ymin>56</ymin>
+			<xmax>233</xmax>
+			<ymax>191</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>206</xmin>
+			<ymin>60</ymin>
+			<xmax>250</xmax>
+			<ymax>147</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>232</xmin>
+			<ymin>62</ymin>
+			<xmax>255</xmax>
+			<ymax>116</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/34.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/34.jpg
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Binary files /dev/null and b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/34.jpg differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/34.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/34.xml
new file mode 100644
index 0000000000000000000000000000000000000000..4b39b925cc17d9c9d98fcc47f013148a7056bd25
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/34.xml	
@@ -0,0 +1,62 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>34.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\34.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>478</width>
+		<height>359</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>237</xmin>
+			<ymin>23</ymin>
+			<xmax>438</xmax>
+			<ymax>313</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>152</xmin>
+			<ymin>88</ymin>
+			<xmax>233</xmax>
+			<ymax>214</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>110</xmin>
+			<ymin>113</ymin>
+			<xmax>166</xmax>
+			<ymax>189</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>85</xmin>
+			<ymin>150</ymin>
+			<xmax>106</xmax>
+			<ymax>186</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/35.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/35.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/35.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/35.xml
new file mode 100644
index 0000000000000000000000000000000000000000..4b6b98479598bb5468aef6531be82c30aa5f8de2
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/35.xml	
@@ -0,0 +1,86 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>35.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\35.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>507</width>
+		<height>338</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>305</xmin>
+			<ymin>19</ymin>
+			<xmax>480</xmax>
+			<ymax>298</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>150</xmin>
+			<ymin>175</ymin>
+			<xmax>212</xmax>
+			<ymax>262</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>125</xmin>
+			<ymin>198</ymin>
+			<xmax>157</xmax>
+			<ymax>257</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>97</xmin>
+			<ymin>215</ymin>
+			<xmax>120</xmax>
+			<ymax>254</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>86</xmin>
+			<ymin>219</ymin>
+			<xmax>101</xmax>
+			<ymax>254</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>52</xmin>
+			<ymin>229</ymin>
+			<xmax>65</xmax>
+			<ymax>249</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/36.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/36.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/36.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/36.xml
new file mode 100644
index 0000000000000000000000000000000000000000..10a88dcb45c48dfcb952ae030b23363ceabe660f
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/36.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>36.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\36.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>323</width>
+		<height>529</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>1</xmin>
+			<ymin>17</ymin>
+			<xmax>309</xmax>
+			<ymax>504</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/37.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/37.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/37.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/37.xml
new file mode 100644
index 0000000000000000000000000000000000000000..e9741a2467c31304a3fb39feb5e3a6b029004b29
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/37.xml	
@@ -0,0 +1,38 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>37.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\37.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>421</width>
+		<height>407</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>43</xmin>
+			<ymin>39</ymin>
+			<xmax>257</xmax>
+			<ymax>301</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>189</xmin>
+			<ymin>115</ymin>
+			<xmax>384</xmax>
+			<ymax>268</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/38.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/38.jpg
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Binary files /dev/null and b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/38.jpg differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/38.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/38.xml
new file mode 100644
index 0000000000000000000000000000000000000000..05e3e59a73a8ed9ab78048e6b9522cc95f2fd82d
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/38.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>38.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\38.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>492</width>
+		<height>348</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>208</xmin>
+			<ymin>25</ymin>
+			<xmax>428</xmax>
+			<ymax>311</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/39.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/39.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/39.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/39.xml
new file mode 100644
index 0000000000000000000000000000000000000000..a66d6d4be73f0712cce547d699e0e885348eeae5
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/39.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>39.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\39.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>469</width>
+		<height>368</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>58</xmin>
+			<ymin>120</ymin>
+			<xmax>329</xmax>
+			<ymax>307</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/4.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/4.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/4.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/4.xml
new file mode 100644
index 0000000000000000000000000000000000000000..6ae7150fa5243bdb61f61c2fc0d7cd3bb73770a2
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/4.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>4.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\4.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>381</width>
+		<height>454</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>14</xmin>
+			<ymin>25</ymin>
+			<xmax>365</xmax>
+			<ymax>432</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/40.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/40.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/40.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/40.xml
new file mode 100644
index 0000000000000000000000000000000000000000..54d5cd8d132225e1425f56d52a92b60c73357d51
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/40.xml	
@@ -0,0 +1,146 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>40.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\40.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>507</width>
+		<height>338</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>51</xmin>
+			<ymin>125</ymin>
+			<xmax>149</xmax>
+			<ymax>300</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>147</xmin>
+			<ymin>108</ymin>
+			<xmax>230</xmax>
+			<ymax>247</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>54</xmin>
+			<ymin>12</ymin>
+			<xmax>84</xmax>
+			<ymax>57</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>150</xmin>
+			<ymin>10</ymin>
+			<xmax>172</xmax>
+			<ymax>48</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>218</xmin>
+			<ymin>8</ymin>
+			<xmax>241</xmax>
+			<ymax>43</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>444</xmin>
+			<ymin>43</ymin>
+			<xmax>479</xmax>
+			<ymax>101</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>305</xmin>
+			<ymin>156</ymin>
+			<xmax>407</xmax>
+			<ymax>254</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>275</xmin>
+			<ymin>10</ymin>
+			<xmax>293</xmax>
+			<ymax>38</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>485</xmin>
+			<ymin>35</ymin>
+			<xmax>507</xmax>
+			<ymax>82</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>314</xmin>
+			<ymin>9</ymin>
+			<xmax>328</xmax>
+			<ymax>35</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>354</xmin>
+			<ymin>9</ymin>
+			<xmax>368</xmax>
+			<ymax>33</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/41.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/41.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/41.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/41.xml
new file mode 100644
index 0000000000000000000000000000000000000000..c49262c9497ef61c6eb8ce92a5ed051d82039973
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/41.xml	
@@ -0,0 +1,122 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>41.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\41.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>529</width>
+		<height>327</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>45</xmin>
+			<ymin>155</ymin>
+			<xmax>149</xmax>
+			<ymax>319</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>152</xmin>
+			<ymin>128</ymin>
+			<xmax>241</xmax>
+			<ymax>277</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>228</xmin>
+			<ymin>106</ymin>
+			<xmax>311</xmax>
+			<ymax>247</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>295</xmin>
+			<ymin>90</ymin>
+			<xmax>368</xmax>
+			<ymax>227</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>339</xmin>
+			<ymin>72</ymin>
+			<xmax>407</xmax>
+			<ymax>200</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>381</xmin>
+			<ymin>61</ymin>
+			<xmax>444</xmax>
+			<ymax>184</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>420</xmin>
+			<ymin>47</ymin>
+			<xmax>477</xmax>
+			<ymax>165</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>464</xmin>
+			<ymin>35</ymin>
+			<xmax>520</xmax>
+			<ymax>145</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>496</xmin>
+			<ymin>22</ymin>
+			<xmax>529</xmax>
+			<ymax>123</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/42.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/42.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/42.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/42.xml
new file mode 100644
index 0000000000000000000000000000000000000000..695e55e16778cb35ea2b99b4b5be45e154091f3e
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/42.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>42.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\42.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>338</width>
+		<height>507</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>66</xmin>
+			<ymin>11</ymin>
+			<xmax>293</xmax>
+			<ymax>492</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/43.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/43.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/43.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/43.xml
new file mode 100644
index 0000000000000000000000000000000000000000..0feb64e1c1383bfefcce73cc193b471ca31805ff
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/43.xml	
@@ -0,0 +1,62 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>43.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\43.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>507</width>
+		<height>338</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>126</xmin>
+			<ymin>267</ymin>
+			<xmax>139</xmax>
+			<ymax>285</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>391</xmin>
+			<ymin>261</ymin>
+			<xmax>402</xmax>
+			<ymax>282</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>264</xmin>
+			<ymin>260</ymin>
+			<xmax>277</xmax>
+			<ymax>281</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>194</xmin>
+			<ymin>261</ymin>
+			<xmax>208</xmax>
+			<ymax>282</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/44.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/44.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/44.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/44.xml
new file mode 100644
index 0000000000000000000000000000000000000000..24bc004131fd66c71d6b739a48f1e22b89a604d3
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/44.xml	
@@ -0,0 +1,50 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>44.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\44.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>337</width>
+		<height>508</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>132</xmin>
+			<ymin>37</ymin>
+			<xmax>275</xmax>
+			<ymax>302</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>2</xmin>
+			<ymin>283</ymin>
+			<xmax>99</xmax>
+			<ymax>508</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>107</xmin>
+			<ymin>1</ymin>
+			<xmax>187</xmax>
+			<ymax>95</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/45.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/45.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/45.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/45.xml
new file mode 100644
index 0000000000000000000000000000000000000000..9fcb50f9ca3c734fa55c734247bb72039fb79423
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/45.xml	
@@ -0,0 +1,98 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>45.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\45.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>594</width>
+		<height>396</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>256</xmin>
+			<ymin>264</ymin>
+			<xmax>308</xmax>
+			<ymax>347</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>115</xmin>
+			<ymin>249</ymin>
+			<xmax>154</xmax>
+			<ymax>317</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>15</xmin>
+			<ymin>240</ymin>
+			<xmax>53</xmax>
+			<ymax>293</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>507</xmin>
+			<ymin>283</ymin>
+			<xmax>573</xmax>
+			<ymax>391</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>347</xmin>
+			<ymin>192</ymin>
+			<xmax>359</xmax>
+			<ymax>209</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>289</xmin>
+			<ymin>190</ymin>
+			<xmax>302</xmax>
+			<ymax>209</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>102</xmin>
+			<ymin>194</ymin>
+			<xmax>111</xmax>
+			<ymax>206</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/46.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/46.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/46.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/46.xml
new file mode 100644
index 0000000000000000000000000000000000000000..04029dac98ce8d36867c69aa932cc8ef5a6da83c
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/46.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>46.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\46.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>338</width>
+		<height>507</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>132</xmin>
+			<ymin>162</ymin>
+			<xmax>304</xmax>
+			<ymax>478</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/47.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/47.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/47.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/47.xml
new file mode 100644
index 0000000000000000000000000000000000000000..3e3aa3d1a0ccc16f7695e16d4739e5028c9a5c5e
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/47.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>47.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\47.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>478</width>
+		<height>359</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>103</xmin>
+			<ymin>11</ymin>
+			<xmax>384</xmax>
+			<ymax>322</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/48.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/48.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/48.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/48.xml
new file mode 100644
index 0000000000000000000000000000000000000000..68b1fdd321847c9d06d4e7e8c343be44866c4465
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/48.xml	
@@ -0,0 +1,62 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>48.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\48.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>514</width>
+		<height>333</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>16</xmin>
+			<ymin>23</ymin>
+			<xmax>80</xmax>
+			<ymax>135</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>113</xmin>
+			<ymin>34</ymin>
+			<xmax>190</xmax>
+			<ymax>164</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>375</xmin>
+			<ymin>90</ymin>
+			<xmax>506</xmax>
+			<ymax>291</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>205</xmin>
+			<ymin>165</ymin>
+			<xmax>363</xmax>
+			<ymax>301</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/49.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/49.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/49.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/49.xml
new file mode 100644
index 0000000000000000000000000000000000000000..7a3761ffd47ef6872354eae63391148063096287
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/49.xml	
@@ -0,0 +1,38 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>49.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\49.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>594</width>
+		<height>334</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>27</xmin>
+			<ymin>44</ymin>
+			<xmax>212</xmax>
+			<ymax>283</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>369</xmin>
+			<ymin>9</ymin>
+			<xmax>589</xmax>
+			<ymax>292</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/5.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/5.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/5.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/5.xml
new file mode 100644
index 0000000000000000000000000000000000000000..5b963a42f1c4e195a6b3a367d1566c326ea3f5e6
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/5.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>5.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\5.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>337</width>
+		<height>508</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>5</xmin>
+			<ymin>38</ymin>
+			<xmax>336</xmax>
+			<ymax>478</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/50.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/50.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/50.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/50.xml
new file mode 100644
index 0000000000000000000000000000000000000000..c396b6031b259f2cf9a0af8bdef12cd59eb45594
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/50.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>50.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\50.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>337</width>
+		<height>508</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>53</xmin>
+			<ymin>47</ymin>
+			<xmax>259</xmax>
+			<ymax>456</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/51.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/51.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/51.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/51.xml
new file mode 100644
index 0000000000000000000000000000000000000000..16d57f31d918a02bda72df976e45fcce5e519c3a
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/51.xml	
@@ -0,0 +1,74 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>51.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\51.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>594</width>
+		<height>401</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>378</xmin>
+			<ymin>246</ymin>
+			<xmax>411</xmax>
+			<ymax>286</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>22</xmin>
+			<ymin>292</ymin>
+			<xmax>79</xmax>
+			<ymax>368</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>178</xmin>
+			<ymin>257</ymin>
+			<xmax>205</xmax>
+			<ymax>309</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>113</xmin>
+			<ymin>263</ymin>
+			<xmax>145</xmax>
+			<ymax>321</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>79</xmin>
+			<ymin>275</ymin>
+			<xmax>117</xmax>
+			<ymax>339</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/52.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/52.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/52.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/52.xml
new file mode 100644
index 0000000000000000000000000000000000000000..80a8ffbf2fac7cd8c62660bea200651d6512c30c
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/52.xml	
@@ -0,0 +1,38 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>52.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\52.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>170</width>
+		<height>106</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>1</xmin>
+			<ymin>1</ymin>
+			<xmax>97</xmax>
+			<ymax>103</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>35</xmin>
+			<ymin>16</ymin>
+			<xmax>161</xmax>
+			<ymax>104</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/53.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/53.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/53.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/53.xml
new file mode 100644
index 0000000000000000000000000000000000000000..a86fb83d76693f31bba0053ec6e6486359612f54
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/53.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>53.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\53.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>114</width>
+		<height>170</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>17</xmin>
+			<ymin>19</ymin>
+			<xmax>92</xmax>
+			<ymax>158</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/54.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/54.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/54.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/54.xml
new file mode 100644
index 0000000000000000000000000000000000000000..7e059368607fb0a24950911c4330ea9ef7128013
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/54.xml	
@@ -0,0 +1,62 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>54.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\54.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>416</width>
+		<height>416</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>309</xmin>
+			<ymin>158</ymin>
+			<xmax>383</xmax>
+			<ymax>264</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>13</xmin>
+			<ymin>161</ymin>
+			<xmax>85</xmax>
+			<ymax>264</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>34</xmin>
+			<ymin>90</ymin>
+			<xmax>95</xmax>
+			<ymax>166</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>276</xmin>
+			<ymin>82</ymin>
+			<xmax>324</xmax>
+			<ymax>156</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/55.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/55.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/55.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/55.xml
new file mode 100644
index 0000000000000000000000000000000000000000..870124701ab6dad222cde82a5b397f7ba9df93b3
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/55.xml	
@@ -0,0 +1,38 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>55.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\55.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>478</width>
+		<height>359</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>177</xmin>
+			<ymin>44</ymin>
+			<xmax>355</xmax>
+			<ymax>286</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>377</xmin>
+			<ymin>164</ymin>
+			<xmax>448</xmax>
+			<ymax>261</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/56.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/56.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/56.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/56.xml
new file mode 100644
index 0000000000000000000000000000000000000000..256b78685e7e218cb345c084807faa46516a4a09
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/56.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>56.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\56.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>422</width>
+		<height>407</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>79</xmin>
+			<ymin>10</ymin>
+			<xmax>306</xmax>
+			<ymax>314</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/57.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/57.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/57.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/57.xml
new file mode 100644
index 0000000000000000000000000000000000000000..70c7754a80ddad37f42d2c093976b8d357f52bb3
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/57.xml	
@@ -0,0 +1,86 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>57.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\57.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>414</width>
+		<height>414</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>23</xmin>
+			<ymin>229</ymin>
+			<xmax>158</xmax>
+			<ymax>394</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>171</xmin>
+			<ymin>190</ymin>
+			<xmax>267</xmax>
+			<ymax>307</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>257</xmin>
+			<ymin>168</ymin>
+			<xmax>330</xmax>
+			<ymax>260</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>311</xmin>
+			<ymin>155</ymin>
+			<xmax>372</xmax>
+			<ymax>229</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>355</xmin>
+			<ymin>145</ymin>
+			<xmax>401</xmax>
+			<ymax>208</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>388</xmin>
+			<ymin>138</ymin>
+			<xmax>414</xmax>
+			<ymax>191</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/58.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/58.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/58.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/58.xml
new file mode 100644
index 0000000000000000000000000000000000000000..d7e5b8a5b216526919367bd777880bcca9c78b82
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/58.xml	
@@ -0,0 +1,38 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>58.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\58.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>369</width>
+		<height>464</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>34</xmin>
+			<ymin>171</ymin>
+			<xmax>193</xmax>
+			<ymax>433</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>148</xmin>
+			<ymin>174</ymin>
+			<xmax>339</xmax>
+			<ymax>284</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/59.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/59.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/59.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/59.xml
new file mode 100644
index 0000000000000000000000000000000000000000..57a3eb5e6d8ed886b287f24400929c36df87820a
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/59.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>59.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\59.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>359</width>
+		<height>478</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>53</xmin>
+			<ymin>57</ymin>
+			<xmax>292</xmax>
+			<ymax>434</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/6.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/6.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/6.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/6.xml
new file mode 100644
index 0000000000000000000000000000000000000000..30164432ff2f73dbdb5dd172572dc99ea59458e7
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/6.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>6.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\6.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>337</width>
+		<height>508</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>6</xmin>
+			<ymin>39</ymin>
+			<xmax>332</xmax>
+			<ymax>468</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/60.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/60.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/60.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/60.xml
new file mode 100644
index 0000000000000000000000000000000000000000..fd820d8c78151e52d0ffb4fc0bbc1cac9db90e2e
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/60.xml	
@@ -0,0 +1,86 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>60.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\60.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>482</width>
+		<height>355</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>50</xmin>
+			<ymin>148</ymin>
+			<xmax>195</xmax>
+			<ymax>337</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>212</xmin>
+			<ymin>109</ymin>
+			<xmax>317</xmax>
+			<ymax>235</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>306</xmin>
+			<ymin>81</ymin>
+			<xmax>389</xmax>
+			<ymax>182</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>368</xmin>
+			<ymin>67</ymin>
+			<xmax>434</xmax>
+			<ymax>149</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>422</xmin>
+			<ymin>54</ymin>
+			<xmax>468</xmax>
+			<ymax>125</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>454</xmin>
+			<ymin>48</ymin>
+			<xmax>482</xmax>
+			<ymax>105</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/61.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/61.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/61.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/61.xml
new file mode 100644
index 0000000000000000000000000000000000000000..234d206c51ad03671c775d091469d1f1a03c1427
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/61.xml	
@@ -0,0 +1,50 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>61.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\61.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>507</width>
+		<height>338</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>57</xmin>
+			<ymin>168</ymin>
+			<xmax>166</xmax>
+			<ymax>319</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>69</xmin>
+			<ymin>125</ymin>
+			<xmax>143</xmax>
+			<ymax>225</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>96</xmin>
+			<ymin>73</ymin>
+			<xmax>139</xmax>
+			<ymax>134</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/62.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/62.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/62.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/62.xml
new file mode 100644
index 0000000000000000000000000000000000000000..6c5e86da09fdff2eda112e4ac2dbefe0b21e3aac
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/62.xml	
@@ -0,0 +1,50 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>62.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\62.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>478</width>
+		<height>359</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>249</xmin>
+			<ymin>182</ymin>
+			<xmax>346</xmax>
+			<ymax>303</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>107</xmin>
+			<ymin>189</ymin>
+			<xmax>223</xmax>
+			<ymax>272</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>48</xmin>
+			<ymin>155</ymin>
+			<xmax>128</xmax>
+			<ymax>259</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/63.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/63.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/63.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/63.xml
new file mode 100644
index 0000000000000000000000000000000000000000..8a22ec396b8b851a79249caf1320e387e82e050f
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/63.xml	
@@ -0,0 +1,62 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>63.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\63.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>478</width>
+		<height>359</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>292</xmin>
+			<ymin>202</ymin>
+			<xmax>398</xmax>
+			<ymax>293</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>130</xmin>
+			<ymin>168</ymin>
+			<xmax>222</xmax>
+			<ymax>270</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>52</xmin>
+			<ymin>210</ymin>
+			<xmax>153</xmax>
+			<ymax>335</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>183</xmin>
+			<ymin>153</ymin>
+			<xmax>256</xmax>
+			<ymax>218</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/64.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/64.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/64.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/64.xml
new file mode 100644
index 0000000000000000000000000000000000000000..856ed467912123196397451dcd2a4559c0a84b61
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/64.xml	
@@ -0,0 +1,62 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>64.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\64.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>359</width>
+		<height>479</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>171</xmin>
+			<ymin>26</ymin>
+			<xmax>254</xmax>
+			<ymax>178</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>2</xmin>
+			<ymin>179</ymin>
+			<xmax>189</xmax>
+			<ymax>474</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>252</xmin>
+			<ymin>1</ymin>
+			<xmax>301</xmax>
+			<ymax>85</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>286</xmin>
+			<ymin>1</ymin>
+			<xmax>322</xmax>
+			<ymax>43</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/65.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/65.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/65.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/65.xml
new file mode 100644
index 0000000000000000000000000000000000000000..802484edee2522128e25d51cbe357c1673f5f584
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/65.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>65.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\65.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>339</width>
+		<height>509</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>24</xmin>
+			<ymin>58</ymin>
+			<xmax>318</xmax>
+			<ymax>471</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/66.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/66.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/66.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/66.xml
new file mode 100644
index 0000000000000000000000000000000000000000..16f99d0bc13f4cb7a6fae51962fdccf12564eee3
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/66.xml	
@@ -0,0 +1,38 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>66.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\66.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>1024</width>
+		<height>680</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>69</xmin>
+			<ymin>257</ymin>
+			<xmax>329</xmax>
+			<ymax>648</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>649</xmin>
+			<ymin>239</ymin>
+			<xmax>922</xmax>
+			<ymax>621</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/67.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/67.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/67.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/67.xml
new file mode 100644
index 0000000000000000000000000000000000000000..f0843121c0f2f17800fad45f4e84d9280d95d58a
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/67.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>67.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\67.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>356</width>
+		<height>481</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>106</xmin>
+			<ymin>88</ymin>
+			<xmax>241</xmax>
+			<ymax>316</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/68.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/68.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/68.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/68.xml
new file mode 100644
index 0000000000000000000000000000000000000000..7cb60fc813748d489aa9e7022bc9f1beb14b4cd2
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/68.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>68.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\68.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>478</width>
+		<height>359</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>221</xmin>
+			<ymin>110</ymin>
+			<xmax>259</xmax>
+			<ymax>179</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/69.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/69.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/69.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/69.xml
new file mode 100644
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/69.xml	
@@ -0,0 +1,38 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>69.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\69.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>478</width>
+		<height>359</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>28</xmin>
+			<ymin>30</ymin>
+			<xmax>240</xmax>
+			<ymax>302</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>178</xmin>
+			<ymin>99</ymin>
+			<xmax>452</xmax>
+			<ymax>312</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/7.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/7.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/7.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/7.xml
new file mode 100644
index 0000000000000000000000000000000000000000..24cdb64b5e9aeeeab9b3272251a371bd210d5955
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/7.xml	
@@ -0,0 +1,98 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>7.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\7.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>507</width>
+		<height>338</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>280</xmin>
+			<ymin>45</ymin>
+			<xmax>438</xmax>
+			<ymax>274</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>79</xmin>
+			<ymin>150</ymin>
+			<xmax>100</xmax>
+			<ymax>186</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>167</xmin>
+			<ymin>154</ymin>
+			<xmax>187</xmax>
+			<ymax>184</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>203</xmin>
+			<ymin>152</ymin>
+			<xmax>219</xmax>
+			<ymax>187</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>255</xmin>
+			<ymin>114</ymin>
+			<xmax>310</xmax>
+			<ymax>221</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>245</xmin>
+			<ymin>137</ymin>
+			<xmax>269</xmax>
+			<ymax>201</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>231</xmin>
+			<ymin>145</ymin>
+			<xmax>250</xmax>
+			<ymax>195</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/70.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/70.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/70.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/70.xml
new file mode 100644
index 0000000000000000000000000000000000000000..0f9684ad168cc5b2038e22b2b070e9da5e38feee
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/70.xml	
@@ -0,0 +1,62 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>70.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\70.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>363</width>
+		<height>473</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>73</xmin>
+			<ymin>232</ymin>
+			<xmax>155</xmax>
+			<ymax>405</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>247</xmin>
+			<ymin>72</ymin>
+			<xmax>323</xmax>
+			<ymax>223</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>188</xmin>
+			<ymin>111</ymin>
+			<xmax>251</xmax>
+			<ymax>269</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>126</xmin>
+			<ymin>166</ymin>
+			<xmax>203</xmax>
+			<ymax>330</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/71.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/71.jpg
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/71.xml	
@@ -0,0 +1,50 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>71.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\71.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>412</width>
+		<height>415</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>177</xmin>
+			<ymin>69</ymin>
+			<xmax>373</xmax>
+			<ymax>393</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>65</xmin>
+			<ymin>166</ymin>
+			<xmax>125</xmax>
+			<ymax>255</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>342</xmin>
+			<ymin>152</ymin>
+			<xmax>412</xmax>
+			<ymax>265</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/72.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/72.jpg
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/72.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>72.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\72.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>338</width>
+		<height>507</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>129</xmin>
+			<ymin>159</ymin>
+			<xmax>301</xmax>
+			<ymax>476</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/73.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/73.jpg
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/73.xml	
@@ -0,0 +1,98 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>73.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\73.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>604</width>
+		<height>283</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>268</xmin>
+			<ymin>113</ymin>
+			<xmax>315</xmax>
+			<ymax>171</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>185</xmin>
+			<ymin>113</ymin>
+			<xmax>233</xmax>
+			<ymax>170</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>103</xmin>
+			<ymin>114</ymin>
+			<xmax>150</xmax>
+			<ymax>170</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>15</xmin>
+			<ymin>114</ymin>
+			<xmax>65</xmax>
+			<ymax>170</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>351</xmin>
+			<ymin>114</ymin>
+			<xmax>402</xmax>
+			<ymax>171</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>440</xmin>
+			<ymin>114</ymin>
+			<xmax>487</xmax>
+			<ymax>171</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>528</xmin>
+			<ymin>116</ymin>
+			<xmax>575</xmax>
+			<ymax>171</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/74.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/74.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/74.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/74.xml
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/74.xml	
@@ -0,0 +1,74 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>74.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\74.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>509</width>
+		<height>339</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>50</xmin>
+			<ymin>1</ymin>
+			<xmax>183</xmax>
+			<ymax>188</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>253</xmin>
+			<ymin>64</ymin>
+			<xmax>366</xmax>
+			<ymax>268</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>339</xmin>
+			<ymin>7</ymin>
+			<xmax>429</xmax>
+			<ymax>179</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>127</xmin>
+			<ymin>99</ymin>
+			<xmax>259</xmax>
+			<ymax>335</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>425</xmin>
+			<ymin>4</ymin>
+			<xmax>509</xmax>
+			<ymax>171</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/75.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/75.jpg
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/75.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>75.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\75.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>416</width>
+		<height>416</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>52</xmin>
+			<ymin>16</ymin>
+			<xmax>354</xmax>
+			<ymax>393</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/76.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/76.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/76.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/76.xml
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/76.xml	
@@ -0,0 +1,38 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>76.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\76.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>492</width>
+		<height>348</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>80</xmin>
+			<ymin>49</ymin>
+			<xmax>295</xmax>
+			<ymax>298</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>216</xmin>
+			<ymin>81</ymin>
+			<xmax>399</xmax>
+			<ymax>271</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/77.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/77.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/77.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/77.xml
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/77.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>77.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\77.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>387</width>
+		<height>443</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>8</xmin>
+			<ymin>1</ymin>
+			<xmax>378</xmax>
+			<ymax>441</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/78.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/78.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/78.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/78.xml
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/78.xml	
@@ -0,0 +1,38 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>78.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\78.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>337</width>
+		<height>506</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>138</xmin>
+			<ymin>164</ymin>
+			<xmax>306</xmax>
+			<ymax>457</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>53</xmin>
+			<ymin>162</ymin>
+			<xmax>109</xmax>
+			<ymax>252</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/79.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/79.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/79.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/79.xml
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/79.xml	
@@ -0,0 +1,62 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>79.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\79.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>505</width>
+		<height>339</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>317</xmin>
+			<ymin>106</ymin>
+			<xmax>435</xmax>
+			<ymax>319</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>267</xmin>
+			<ymin>140</ymin>
+			<xmax>340</xmax>
+			<ymax>266</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>239</xmin>
+			<ymin>167</ymin>
+			<xmax>272</xmax>
+			<ymax>233</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>419</xmin>
+			<ymin>164</ymin>
+			<xmax>470</xmax>
+			<ymax>253</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/8.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/8.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/8.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/8.xml
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/8.xml	
@@ -0,0 +1,74 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>8.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\8.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>507</width>
+		<height>338</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>332</xmin>
+			<ymin>52</ymin>
+			<xmax>460</xmax>
+			<ymax>295</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>207</xmin>
+			<ymin>131</ymin>
+			<xmax>242</xmax>
+			<ymax>205</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>169</xmin>
+			<ymin>147</ymin>
+			<xmax>192</xmax>
+			<ymax>191</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>155</xmin>
+			<ymin>155</ymin>
+			<xmax>169</xmax>
+			<ymax>183</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>47</xmin>
+			<ymin>167</ymin>
+			<xmax>52</xmax>
+			<ymax>174</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/80.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/80.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/80.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/80.xml
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/80.xml	
@@ -0,0 +1,62 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>80.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\80.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>366</width>
+		<height>467</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>98</xmin>
+			<ymin>211</ymin>
+			<xmax>182</xmax>
+			<ymax>357</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>99</xmin>
+			<ymin>202</ymin>
+			<xmax>148</xmax>
+			<ymax>299</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>134</xmin>
+			<ymin>241</ymin>
+			<xmax>228</xmax>
+			<ymax>465</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>108</xmin>
+			<ymin>195</ymin>
+			<xmax>133</xmax>
+			<ymax>249</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/81.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/81.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/81.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/81.xml
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/81.xml	
@@ -0,0 +1,62 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>81.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\81.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>414</width>
+		<height>414</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>78</xmin>
+			<ymin>55</ymin>
+			<xmax>260</xmax>
+			<ymax>308</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>257</xmin>
+			<ymin>121</ymin>
+			<xmax>356</xmax>
+			<ymax>272</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>233</xmin>
+			<ymin>180</ymin>
+			<xmax>278</xmax>
+			<ymax>247</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>43</xmin>
+			<ymin>160</ymin>
+			<xmax>123</xmax>
+			<ymax>253</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/82.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/82.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/82.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/82.xml
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/82.xml	
@@ -0,0 +1,62 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>82.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\82.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>514</width>
+		<height>333</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>105</xmin>
+			<ymin>33</ymin>
+			<xmax>191</xmax>
+			<ymax>163</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>13</xmin>
+			<ymin>25</ymin>
+			<xmax>83</xmax>
+			<ymax>136</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>376</xmin>
+			<ymin>90</ymin>
+			<xmax>506</xmax>
+			<ymax>292</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>205</xmin>
+			<ymin>168</ymin>
+			<xmax>363</xmax>
+			<ymax>296</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/83.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/83.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/83.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/83.xml
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/83.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>83.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\83.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>509</width>
+		<height>339</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>252</xmin>
+			<ymin>1</ymin>
+			<xmax>509</xmax>
+			<ymax>336</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/84.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/84.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/84.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/84.xml
new file mode 100644
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/84.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>84.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\84.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>509</width>
+		<height>339</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>192</xmin>
+			<ymin>189</ymin>
+			<xmax>333</xmax>
+			<ymax>325</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/85.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/85.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/85.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/85.xml
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/85.xml	
@@ -0,0 +1,62 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>85.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\85.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>509</width>
+		<height>339</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>146</xmin>
+			<ymin>71</ymin>
+			<xmax>228</xmax>
+			<ymax>215</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>294</xmin>
+			<ymin>28</ymin>
+			<xmax>407</xmax>
+			<ymax>187</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>38</xmin>
+			<ymin>109</ymin>
+			<xmax>114</xmax>
+			<ymax>232</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>408</xmin>
+			<ymin>73</ymin>
+			<xmax>497</xmax>
+			<ymax>222</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/86.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/86.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/86.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/86.xml
new file mode 100644
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/86.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>86.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\86.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>507</width>
+		<height>338</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>193</xmin>
+			<ymin>12</ymin>
+			<xmax>457</xmax>
+			<ymax>275</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/87.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/87.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/87.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/87.xml
new file mode 100644
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/87.xml	
@@ -0,0 +1,62 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>87.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\87.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>339</width>
+		<height>506</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>159</xmin>
+			<ymin>223</ymin>
+			<xmax>287</xmax>
+			<ymax>454</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>39</xmin>
+			<ymin>40</ymin>
+			<xmax>77</xmax>
+			<ymax>91</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>114</xmin>
+			<ymin>5</ymin>
+			<xmax>143</xmax>
+			<ymax>43</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>165</xmin>
+			<ymin>4</ymin>
+			<xmax>178</xmax>
+			<ymax>25</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/88.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/88.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/88.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/88.xml
new file mode 100644
index 0000000000000000000000000000000000000000..f61101ba72552659d6c4480315f3c96bf656c086
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/88.xml	
@@ -0,0 +1,62 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>88.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\88.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>478</width>
+		<height>359</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>1</xmin>
+			<ymin>52</ymin>
+			<xmax>106</xmax>
+			<ymax>355</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>204</xmin>
+			<ymin>125</ymin>
+			<xmax>234</xmax>
+			<ymax>193</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>231</xmin>
+			<ymin>139</ymin>
+			<xmax>257</xmax>
+			<ymax>180</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>249</xmin>
+			<ymin>145</ymin>
+			<xmax>261</xmax>
+			<ymax>170</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/89.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/89.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/89.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/89.xml
new file mode 100644
index 0000000000000000000000000000000000000000..b953d1ecb6ff011c9f2ade1f3ac49ab87e56a7cd
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/89.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>89.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\89.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>338</width>
+		<height>507</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>62</xmin>
+			<ymin>10</ymin>
+			<xmax>291</xmax>
+			<ymax>492</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/9.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/9.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/9.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/9.xml
new file mode 100644
index 0000000000000000000000000000000000000000..fd785042d0bb01466c4f88da84cf1199d672ef50
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/9.xml	
@@ -0,0 +1,50 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>9.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\9.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>507</width>
+		<height>338</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>359</xmin>
+			<ymin>94</ymin>
+			<xmax>484</xmax>
+			<ymax>293</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>276</xmin>
+			<ymin>104</ymin>
+			<xmax>362</xmax>
+			<ymax>257</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>120</xmin>
+			<ymin>168</ymin>
+			<xmax>302</xmax>
+			<ymax>293</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/90.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/90.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/90.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/90.xml
new file mode 100644
index 0000000000000000000000000000000000000000..e40077788f69f2e374503b6d5d0bea7abaec493e
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/90.xml	
@@ -0,0 +1,98 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>90.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\90.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>535</width>
+		<height>322</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>220</xmin>
+			<ymin>203</ymin>
+			<xmax>258</xmax>
+			<ymax>250</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>121</xmin>
+			<ymin>213</ymin>
+			<xmax>135</xmax>
+			<ymax>241</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>70</xmin>
+			<ymin>203</ymin>
+			<xmax>103</xmax>
+			<ymax>244</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>6</xmin>
+			<ymin>213</ymin>
+			<xmax>30</xmax>
+			<ymax>243</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>308</xmin>
+			<ymin>177</ymin>
+			<xmax>359</xmax>
+			<ymax>261</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>396</xmin>
+			<ymin>221</ymin>
+			<xmax>415</xmax>
+			<ymax>241</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>458</xmin>
+			<ymin>224</ymin>
+			<xmax>470</xmax>
+			<ymax>241</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/91.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/91.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/91.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/91.xml
new file mode 100644
index 0000000000000000000000000000000000000000..84472c691496745cc5faab9a0176fa1f32bbff79
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/91.xml	
@@ -0,0 +1,50 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>91.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\91.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>553</width>
+		<height>311</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>148</xmin>
+			<ymin>27</ymin>
+			<xmax>262</xmax>
+			<ymax>161</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>257</xmin>
+			<ymin>1</ymin>
+			<xmax>350</xmax>
+			<ymax>137</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>1</xmin>
+			<ymin>1</ymin>
+			<xmax>122</xmax>
+			<ymax>88</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/92.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/92.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/92.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/92.xml
new file mode 100644
index 0000000000000000000000000000000000000000..f7f18b524196b3a3caea2cbb6998a95ee8cf8ad7
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/92.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>92.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\92.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>359</width>
+		<height>479</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>69</xmin>
+			<ymin>190</ymin>
+			<xmax>223</xmax>
+			<ymax>389</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/93.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/93.jpg
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Binary files /dev/null and b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/93.jpg differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/93.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/93.xml
new file mode 100644
index 0000000000000000000000000000000000000000..3805ae278700271f60d861a76293f88457c2e235
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/93.xml	
@@ -0,0 +1,62 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>93.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\93.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>337</width>
+		<height>507</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>91</xmin>
+			<ymin>39</ymin>
+			<xmax>178</xmax>
+			<ymax>161</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>3</xmin>
+			<ymin>28</ymin>
+			<xmax>76</xmax>
+			<ymax>128</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>223</xmin>
+			<ymin>60</ymin>
+			<xmax>327</xmax>
+			<ymax>210</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>259</xmin>
+			<ymin>4</ymin>
+			<xmax>290</xmax>
+			<ymax>53</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/94.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/94.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/94.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/94.xml
new file mode 100644
index 0000000000000000000000000000000000000000..7337dca0b4b3370976e3e7227bb8778a8b687e5f
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/94.xml	
@@ -0,0 +1,62 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>94.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\94.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>507</width>
+		<height>339</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>218</xmin>
+			<ymin>24</ymin>
+			<xmax>397</xmax>
+			<ymax>294</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>130</xmin>
+			<ymin>100</ymin>
+			<xmax>183</xmax>
+			<ymax>188</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>111</xmin>
+			<ymin>120</ymin>
+			<xmax>138</xmax>
+			<ymax>167</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>101</xmin>
+			<ymin>125</ymin>
+			<xmax>122</xmax>
+			<ymax>156</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/95.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/95.jpg
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Binary files /dev/null and b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/95.jpg differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/95.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/95.xml
new file mode 100644
index 0000000000000000000000000000000000000000..c29900471df0f112799cada8017beefbc06bb433
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/95.xml	
@@ -0,0 +1,110 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>95.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\95.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>510</width>
+		<height>337</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>312</xmin>
+			<ymin>168</ymin>
+			<xmax>402</xmax>
+			<ymax>325</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>430</xmin>
+			<ymin>23</ymin>
+			<xmax>462</xmax>
+			<ymax>75</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>366</xmin>
+			<ymin>22</ymin>
+			<xmax>399</xmax>
+			<ymax>77</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>311</xmin>
+			<ymin>23</ymin>
+			<xmax>341</xmax>
+			<ymax>75</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>292</xmin>
+			<ymin>117</ymin>
+			<xmax>366</xmax>
+			<ymax>248</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>280</xmin>
+			<ymin>92</ymin>
+			<xmax>333</xmax>
+			<ymax>202</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>121</xmin>
+			<ymin>16</ymin>
+			<xmax>155</xmax>
+			<ymax>65</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>65</xmin>
+			<ymin>16</ymin>
+			<xmax>106</xmax>
+			<ymax>66</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/96.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/96.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..92f76386f69f98b22d2f1b83e534a5e0e307dd56
Binary files /dev/null and b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/96.jpg differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/96.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/96.xml
new file mode 100644
index 0000000000000000000000000000000000000000..23a5390244b0c27ab51f5126142e283bd16b3850
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/96.xml	
@@ -0,0 +1,62 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>96.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\96.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>505</width>
+		<height>342</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>317</xmin>
+			<ymin>113</ymin>
+			<xmax>373</xmax>
+			<ymax>171</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>189</xmin>
+			<ymin>94</ymin>
+			<xmax>255</xmax>
+			<ymax>176</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>218</xmin>
+			<ymin>183</ymin>
+			<xmax>421</xmax>
+			<ymax>320</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>263</xmin>
+			<ymin>96</ymin>
+			<xmax>289</xmax>
+			<ymax>137</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/97.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/97.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..a8e9cdc28f8f8d7852f781f913b0adfc3fda53ce
Binary files /dev/null and b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/97.jpg differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/97.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/97.xml
new file mode 100644
index 0000000000000000000000000000000000000000..3c2c10de8201fd621b1d60bd819d4c7c097c8296
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/97.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>97.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\97.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>432</width>
+		<height>400</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>97</xmin>
+			<ymin>1</ymin>
+			<xmax>166</xmax>
+			<ymax>108</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/98.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/98.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..e03c9664414ee18aec9e61db6d64d80e2bb6c3a2
Binary files /dev/null and b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/98.jpg differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/98.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/98.xml
new file mode 100644
index 0000000000000000000000000000000000000000..8e096381babb4e30abbbb42573548353f5cfa28d
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/98.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>98.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\98.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>509</width>
+		<height>339</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>179</xmin>
+			<ymin>43</ymin>
+			<xmax>255</xmax>
+			<ymax>167</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/99.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/99.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..7a810e26f4e31ca7dcac781532854e5ca0e3c112
Binary files /dev/null and b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/99.jpg differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/99.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/99.xml
new file mode 100644
index 0000000000000000000000000000000000000000..fcae8467e04e5d6e324cd8b075ab9e0affe9c2e5
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/99.xml	
@@ -0,0 +1,86 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>99.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\99.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>476</width>
+		<height>362</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>201</xmin>
+			<ymin>170</ymin>
+			<xmax>294</xmax>
+			<ymax>330</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>231</xmin>
+			<ymin>156</ymin>
+			<xmax>306</xmax>
+			<ymax>297</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>266</xmin>
+			<ymin>139</ymin>
+			<xmax>342</xmax>
+			<ymax>265</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>308</xmin>
+			<ymin>128</ymin>
+			<xmax>370</xmax>
+			<ymax>241</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>328</xmin>
+			<ymin>117</ymin>
+			<xmax>388</xmax>
+			<ymax>217</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>372</xmin>
+			<ymin>101</ymin>
+			<xmax>421</xmax>
+			<ymax>186</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/deep_sort/detection.py b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/deep_sort/detection.py
new file mode 100644
index 0000000000000000000000000000000000000000..d2c48203fca982f1c27a6c8378ece242cd133f99
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/deep_sort/detection.py	
@@ -0,0 +1,55 @@
+# vim: expandtab:ts=4:sw=4
+import numpy as np
+
+
+class Detection(object):
+    """
+    This class represents a bounding box detection in a single image.
+
+    Parameters
+    ----------
+    tlwh : array_like
+        Bounding box in format `(x, y, w, h)`.
+    confidence : float
+        Detector confidence score.
+    feature : array_like
+        A feature vector that describes the object contained in this image.
+
+    Attributes
+    ----------
+    tlwh : ndarray
+        Bounding box in format `(top left x, top left y, width, height)`.
+    confidence : ndarray
+        Detector confidence score.
+    class_name : ndarray
+        Detector class.
+    feature : ndarray | NoneType
+        A feature vector that describes the object contained in this image.
+
+    """
+
+    def __init__(self, tlwh, confidence, class_name, feature):
+        self.tlwh = np.asarray(tlwh, dtype=np.float)
+        self.confidence = float(confidence)
+        self.class_name = class_name
+        self.feature = np.asarray(feature, dtype=np.float32)
+
+    def get_class(self):
+        return self.class_name
+
+    def to_tlbr(self):
+        """Convert bounding box to format `(min x, min y, max x, max y)`, i.e.,
+        `(top left, bottom right)`.
+        """
+        ret = self.tlwh.copy()
+        ret[2:] += ret[:2]
+        return ret
+
+    def to_xyah(self):
+        """Convert bounding box to format `(center x, center y, aspect ratio,
+        height)`, where the aspect ratio is `width / height`.
+        """
+        ret = self.tlwh.copy()
+        ret[:2] += ret[2:] / 2
+        ret[2] /= ret[3]
+        return ret
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/deep_sort/generate_detections.py b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/deep_sort/generate_detections.py
new file mode 100644
index 0000000000000000000000000000000000000000..287a7ad0e64203dc19a8d07c88b18b274c974d3f
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/deep_sort/generate_detections.py	
@@ -0,0 +1,216 @@
+# vim: expandtab:ts=4:sw=4
+import os
+import errno
+import argparse
+import numpy as np
+import cv2
+import tensorflow.compat.v1 as tf
+    
+physical_devices = tf.config.experimental.list_physical_devices('GPU')
+if len(physical_devices) > 0:
+    tf.config.experimental.set_memory_growth(physical_devices[0], True)
+
+def _run_in_batches(f, data_dict, out, batch_size):
+    data_len = len(out)
+    num_batches = int(data_len / batch_size)
+
+    s, e = 0, 0
+    for i in range(num_batches):
+        s, e = i * batch_size, (i + 1) * batch_size
+        batch_data_dict = {k: v[s:e] for k, v in data_dict.items()}
+        out[s:e] = f(batch_data_dict)
+    if e < len(out):
+        batch_data_dict = {k: v[e:] for k, v in data_dict.items()}
+        out[e:] = f(batch_data_dict)
+
+
+def extract_image_patch(image, bbox, patch_shape):
+    """Extract image patch from bounding box.
+
+    Parameters
+    ----------
+    image : ndarray
+        The full image.
+    bbox : array_like
+        The bounding box in format (x, y, width, height).
+    patch_shape : Optional[array_like]
+        This parameter can be used to enforce a desired patch shape
+        (height, width). First, the `bbox` is adapted to the aspect ratio
+        of the patch shape, then it is clipped at the image boundaries.
+        If None, the shape is computed from :arg:`bbox`.
+
+    Returns
+    -------
+    ndarray | NoneType
+        An image patch showing the :arg:`bbox`, optionally reshaped to
+        :arg:`patch_shape`.
+        Returns None if the bounding box is empty or fully outside of the image
+        boundaries.
+
+    """
+    bbox = np.array(bbox)
+    if patch_shape is not None:
+        # correct aspect ratio to patch shape
+        target_aspect = float(patch_shape[1]) / patch_shape[0]
+        new_width = target_aspect * bbox[3]
+        bbox[0] -= (new_width - bbox[2]) / 2
+        bbox[2] = new_width
+
+    # convert to top left, bottom right
+    bbox[2:] += bbox[:2]
+    bbox = bbox.astype(np.int)
+
+    # clip at image boundaries
+    bbox[:2] = np.maximum(0, bbox[:2])
+    bbox[2:] = np.minimum(np.asarray(image.shape[:2][::-1]) - 1, bbox[2:])
+    if np.any(bbox[:2] >= bbox[2:]):
+        return None
+    sx, sy, ex, ey = bbox
+    image = image[sy:ey, sx:ex]
+    image = cv2.resize(image, tuple(patch_shape[::-1]))
+    return image
+
+
+class ImageEncoder(object):
+
+    def __init__(self, checkpoint_filename, input_name="images", output_name="features"):
+        self.session = tf.Session()
+        with tf.gfile.GFile(checkpoint_filename, "rb") as file_handle:
+            graph_def = tf.GraphDef()
+            graph_def.ParseFromString(file_handle.read())
+        tf.import_graph_def(graph_def)
+        try:
+            self.input_var = tf.get_default_graph().get_tensor_by_name(input_name)
+            self.output_var = tf.get_default_graph().get_tensor_by_name(output_name)
+        except KeyError:
+            layers = [i.name for i in tf.get_default_graph().get_operations()]
+            self.input_var = tf.get_default_graph().get_tensor_by_name(layers[0]+':0')
+            self.output_var = tf.get_default_graph().get_tensor_by_name(layers[-1]+':0')            
+
+        assert len(self.output_var.get_shape()) == 2
+        assert len(self.input_var.get_shape()) == 4
+        self.feature_dim = self.output_var.get_shape().as_list()[-1]
+        self.image_shape = self.input_var.get_shape().as_list()[1:]
+
+    def __call__(self, data_x, batch_size=32):
+        out = np.zeros((len(data_x), self.feature_dim), np.float32)
+        _run_in_batches(
+            lambda x: self.session.run(self.output_var, feed_dict=x),
+            {self.input_var: data_x}, out, batch_size)
+        return out
+
+
+def create_box_encoder(model_filename, input_name="images:0", output_name="features:0", batch_size=32):
+    image_encoder = ImageEncoder(model_filename, input_name, output_name)
+    image_shape = image_encoder.image_shape
+
+    def encoder(image, boxes):
+        image_patches = []
+        for box in boxes:
+            patch = extract_image_patch(image, box, image_shape[:2])
+            if patch is None:
+                print("WARNING: Failed to extract image patch: %s." % str(box))
+                patch = np.random.uniform(0., 255., image_shape).astype(np.uint8)
+            image_patches.append(patch)
+        image_patches = np.asarray(image_patches)
+        return image_encoder(image_patches, batch_size)
+
+    return encoder
+
+
+def generate_detections(encoder, mot_dir, output_dir, detection_dir=None):
+    """Generate detections with features.
+
+    Parameters
+    ----------
+    encoder : Callable[image, ndarray] -> ndarray
+        The encoder function takes as input a BGR color image and a matrix of
+        bounding boxes in format `(x, y, w, h)` and returns a matrix of
+        corresponding feature vectors.
+    mot_dir : str
+        Path to the MOTChallenge directory (can be either train or test).
+    output_dir
+        Path to the output directory. Will be created if it does not exist.
+    detection_dir
+        Path to custom detections. The directory structure should be the default
+        MOTChallenge structure: `[sequence]/det/det.txt`. If None, uses the
+        standard MOTChallenge detections.
+
+    """
+    if detection_dir is None:
+        detection_dir = mot_dir
+    try:
+        os.makedirs(output_dir)
+    except OSError as exception:
+        if exception.errno == errno.EEXIST and os.path.isdir(output_dir):
+            pass
+        else:
+            raise ValueError(
+                "Failed to created output directory '%s'" % output_dir)
+
+    for sequence in os.listdir(mot_dir):
+        print("Processing %s" % sequence)
+        sequence_dir = os.path.join(mot_dir, sequence)
+
+        image_dir = os.path.join(sequence_dir, "img1")
+        image_filenames = {
+            int(os.path.splitext(f)[0]): os.path.join(image_dir, f)
+            for f in os.listdir(image_dir)}
+
+        detection_file = os.path.join(
+            detection_dir, sequence, "det/det.txt")
+        detections_in = np.loadtxt(detection_file, delimiter=',')
+        detections_out = []
+
+        frame_indices = detections_in[:, 0].astype(np.int)
+        min_frame_idx = frame_indices.astype(np.int).min()
+        max_frame_idx = frame_indices.astype(np.int).max()
+        for frame_idx in range(min_frame_idx, max_frame_idx + 1):
+            print("Frame %05d/%05d" % (frame_idx, max_frame_idx))
+            mask = frame_indices == frame_idx
+            rows = detections_in[mask]
+
+            if frame_idx not in image_filenames:
+                print("WARNING could not find image for frame %d" % frame_idx)
+                continue
+            bgr_image = cv2.imread(
+                image_filenames[frame_idx], cv2.IMREAD_COLOR)
+            features = encoder(bgr_image, rows[:, 2:6].copy())
+            detections_out += [np.r_[(row, feature)] for row, feature
+                               in zip(rows, features)]
+
+        output_filename = os.path.join(output_dir, "%s.npy" % sequence)
+        np.save(
+            output_filename, np.asarray(detections_out), allow_pickle=False)
+
+
+def parse_args():
+    """Parse command line arguments.
+    """
+    parser = argparse.ArgumentParser(description="Re-ID feature extractor")
+    parser.add_argument(
+        "--model",
+        default="resources/networks/mars-small128.pb",
+        help="Path to freezed inference graph protobuf.")
+    parser.add_argument(
+        "--mot_dir", help="Path to MOTChallenge directory (train or test)",
+        required=True)
+    parser.add_argument(
+        "--detection_dir", help="Path to custom detections. Defaults to "
+        "standard MOT detections Directory structure should be the default "
+        "MOTChallenge structure: [sequence]/det/det.txt", default=None)
+    parser.add_argument(
+        "--output_dir", help="Output directory. Will be created if it does not"
+        " exist.", default="detections")
+    return parser.parse_args()
+
+
+def main():
+    args = parse_args()
+    encoder = create_box_encoder(args.model, batch_size=32)
+    generate_detections(encoder, args.mot_dir, args.output_dir,
+                        args.detection_dir)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/deep_sort/iou_matching.py b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/deep_sort/iou_matching.py
new file mode 100644
index 0000000000000000000000000000000000000000..c4dd0b88391d2ac28036c7163d5d4c988d4a9c4c
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/deep_sort/iou_matching.py	
@@ -0,0 +1,81 @@
+# vim: expandtab:ts=4:sw=4
+from __future__ import absolute_import
+import numpy as np
+from . import linear_assignment
+
+
+def iou(bbox, candidates):
+    """Computer intersection over union.
+
+    Parameters
+    ----------
+    bbox : ndarray
+        A bounding box in format `(top left x, top left y, width, height)`.
+    candidates : ndarray
+        A matrix of candidate bounding boxes (one per row) in the same format
+        as `bbox`.
+
+    Returns
+    -------
+    ndarray
+        The intersection over union in [0, 1] between the `bbox` and each
+        candidate. A higher score means a larger fraction of the `bbox` is
+        occluded by the candidate.
+
+    """
+    bbox_tl, bbox_br = bbox[:2], bbox[:2] + bbox[2:]
+    candidates_tl = candidates[:, :2]
+    candidates_br = candidates[:, :2] + candidates[:, 2:]
+
+    tl = np.c_[np.maximum(bbox_tl[0], candidates_tl[:, 0])[:, np.newaxis],
+               np.maximum(bbox_tl[1], candidates_tl[:, 1])[:, np.newaxis]]
+    br = np.c_[np.minimum(bbox_br[0], candidates_br[:, 0])[:, np.newaxis],
+               np.minimum(bbox_br[1], candidates_br[:, 1])[:, np.newaxis]]
+    wh = np.maximum(0., br - tl)
+
+    area_intersection = wh.prod(axis=1)
+    area_bbox = bbox[2:].prod()
+    area_candidates = candidates[:, 2:].prod(axis=1)
+    return area_intersection / (area_bbox + area_candidates - area_intersection)
+
+
+def iou_cost(tracks, detections, track_indices=None,
+             detection_indices=None):
+    """An intersection over union distance metric.
+
+    Parameters
+    ----------
+    tracks : List[deep_sort.track.Track]
+        A list of tracks.
+    detections : List[deep_sort.detection.Detection]
+        A list of detections.
+    track_indices : Optional[List[int]]
+        A list of indices to tracks that should be matched. Defaults to
+        all `tracks`.
+    detection_indices : Optional[List[int]]
+        A list of indices to detections that should be matched. Defaults
+        to all `detections`.
+
+    Returns
+    -------
+    ndarray
+        Returns a cost matrix of shape
+        len(track_indices), len(detection_indices) where entry (i, j) is
+        `1 - iou(tracks[track_indices[i]], detections[detection_indices[j]])`.
+
+    """
+    if track_indices is None:
+        track_indices = np.arange(len(tracks))
+    if detection_indices is None:
+        detection_indices = np.arange(len(detections))
+
+    cost_matrix = np.zeros((len(track_indices), len(detection_indices)))
+    for row, track_idx in enumerate(track_indices):
+        if tracks[track_idx].time_since_update > 1:
+            cost_matrix[row, :] = linear_assignment.INFTY_COST
+            continue
+
+        bbox = tracks[track_idx].to_tlwh()
+        candidates = np.asarray([detections[i].tlwh for i in detection_indices])
+        cost_matrix[row, :] = 1. - iou(bbox, candidates)
+    return cost_matrix
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/deep_sort/kalman_filter.py b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/deep_sort/kalman_filter.py
new file mode 100644
index 0000000000000000000000000000000000000000..787a76e6a43870a9538647b51fda6a5254ce2d43
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/deep_sort/kalman_filter.py	
@@ -0,0 +1,229 @@
+# vim: expandtab:ts=4:sw=4
+import numpy as np
+import scipy.linalg
+
+
+"""
+Table for the 0.95 quantile of the chi-square distribution with N degrees of
+freedom (contains values for N=1, ..., 9). Taken from MATLAB/Octave's chi2inv
+function and used as Mahalanobis gating threshold.
+"""
+chi2inv95 = {
+    1: 3.8415,
+    2: 5.9915,
+    3: 7.8147,
+    4: 9.4877,
+    5: 11.070,
+    6: 12.592,
+    7: 14.067,
+    8: 15.507,
+    9: 16.919}
+
+
+class KalmanFilter(object):
+    """
+    A simple Kalman filter for tracking bounding boxes in image space.
+
+    The 8-dimensional state space
+
+        x, y, a, h, vx, vy, va, vh
+
+    contains the bounding box center position (x, y), aspect ratio a, height h,
+    and their respective velocities.
+
+    Object motion follows a constant velocity model. The bounding box location
+    (x, y, a, h) is taken as direct observation of the state space (linear
+    observation model).
+
+    """
+
+    def __init__(self):
+        ndim, dt = 4, 1.
+
+        # Create Kalman filter model matrices.
+        self._motion_mat = np.eye(2 * ndim, 2 * ndim)
+        for i in range(ndim):
+            self._motion_mat[i, ndim + i] = dt
+        self._update_mat = np.eye(ndim, 2 * ndim)
+
+        # Motion and observation uncertainty are chosen relative to the current
+        # state estimate. These weights control the amount of uncertainty in
+        # the model. This is a bit hacky.
+        self._std_weight_position = 1. / 20
+        self._std_weight_velocity = 1. / 160
+
+    def initiate(self, measurement):
+        """Create track from unassociated measurement.
+
+        Parameters
+        ----------
+        measurement : ndarray
+            Bounding box coordinates (x, y, a, h) with center position (x, y),
+            aspect ratio a, and height h.
+
+        Returns
+        -------
+        (ndarray, ndarray)
+            Returns the mean vector (8 dimensional) and covariance matrix (8x8
+            dimensional) of the new track. Unobserved velocities are initialized
+            to 0 mean.
+
+        """
+        mean_pos = measurement
+        mean_vel = np.zeros_like(mean_pos)
+        mean = np.r_[mean_pos, mean_vel]
+
+        std = [
+            2 * self._std_weight_position * measurement[3],
+            2 * self._std_weight_position * measurement[3],
+            1e-2,
+            2 * self._std_weight_position * measurement[3],
+            10 * self._std_weight_velocity * measurement[3],
+            10 * self._std_weight_velocity * measurement[3],
+            1e-5,
+            10 * self._std_weight_velocity * measurement[3]]
+        covariance = np.diag(np.square(std))
+        return mean, covariance
+
+    def predict(self, mean, covariance):
+        """Run Kalman filter prediction step.
+
+        Parameters
+        ----------
+        mean : ndarray
+            The 8 dimensional mean vector of the object state at the previous
+            time step.
+        covariance : ndarray
+            The 8x8 dimensional covariance matrix of the object state at the
+            previous time step.
+
+        Returns
+        -------
+        (ndarray, ndarray)
+            Returns the mean vector and covariance matrix of the predicted
+            state. Unobserved velocities are initialized to 0 mean.
+
+        """
+        std_pos = [
+            self._std_weight_position * mean[3],
+            self._std_weight_position * mean[3],
+            1e-2,
+            self._std_weight_position * mean[3]]
+        std_vel = [
+            self._std_weight_velocity * mean[3],
+            self._std_weight_velocity * mean[3],
+            1e-5,
+            self._std_weight_velocity * mean[3]]
+        motion_cov = np.diag(np.square(np.r_[std_pos, std_vel]))
+
+        mean = np.dot(self._motion_mat, mean)
+        covariance = np.linalg.multi_dot((
+            self._motion_mat, covariance, self._motion_mat.T)) + motion_cov
+
+        return mean, covariance
+
+    def project(self, mean, covariance):
+        """Project state distribution to measurement space.
+
+        Parameters
+        ----------
+        mean : ndarray
+            The state's mean vector (8 dimensional array).
+        covariance : ndarray
+            The state's covariance matrix (8x8 dimensional).
+
+        Returns
+        -------
+        (ndarray, ndarray)
+            Returns the projected mean and covariance matrix of the given state
+            estimate.
+
+        """
+        std = [
+            self._std_weight_position * mean[3],
+            self._std_weight_position * mean[3],
+            1e-1,
+            self._std_weight_position * mean[3]]
+        innovation_cov = np.diag(np.square(std))
+
+        mean = np.dot(self._update_mat, mean)
+        covariance = np.linalg.multi_dot((
+            self._update_mat, covariance, self._update_mat.T))
+        return mean, covariance + innovation_cov
+
+    def update(self, mean, covariance, measurement):
+        """Run Kalman filter correction step.
+
+        Parameters
+        ----------
+        mean : ndarray
+            The predicted state's mean vector (8 dimensional).
+        covariance : ndarray
+            The state's covariance matrix (8x8 dimensional).
+        measurement : ndarray
+            The 4 dimensional measurement vector (x, y, a, h), where (x, y)
+            is the center position, a the aspect ratio, and h the height of the
+            bounding box.
+
+        Returns
+        -------
+        (ndarray, ndarray)
+            Returns the measurement-corrected state distribution.
+
+        """
+        projected_mean, projected_cov = self.project(mean, covariance)
+
+        chol_factor, lower = scipy.linalg.cho_factor(
+            projected_cov, lower=True, check_finite=False)
+        kalman_gain = scipy.linalg.cho_solve(
+            (chol_factor, lower), np.dot(covariance, self._update_mat.T).T,
+            check_finite=False).T
+        innovation = measurement - projected_mean
+
+        new_mean = mean + np.dot(innovation, kalman_gain.T)
+        new_covariance = covariance - np.linalg.multi_dot((
+            kalman_gain, projected_cov, kalman_gain.T))
+        return new_mean, new_covariance
+
+    def gating_distance(self, mean, covariance, measurements,
+                        only_position=False):
+        """Compute gating distance between state distribution and measurements.
+
+        A suitable distance threshold can be obtained from `chi2inv95`. If
+        `only_position` is False, the chi-square distribution has 4 degrees of
+        freedom, otherwise 2.
+
+        Parameters
+        ----------
+        mean : ndarray
+            Mean vector over the state distribution (8 dimensional).
+        covariance : ndarray
+            Covariance of the state distribution (8x8 dimensional).
+        measurements : ndarray
+            An Nx4 dimensional matrix of N measurements, each in
+            format (x, y, a, h) where (x, y) is the bounding box center
+            position, a the aspect ratio, and h the height.
+        only_position : Optional[bool]
+            If True, distance computation is done with respect to the bounding
+            box center position only.
+
+        Returns
+        -------
+        ndarray
+            Returns an array of length N, where the i-th element contains the
+            squared Mahalanobis distance between (mean, covariance) and
+            `measurements[i]`.
+
+        """
+        mean, covariance = self.project(mean, covariance)
+        if only_position:
+            mean, covariance = mean[:2], covariance[:2, :2]
+            measurements = measurements[:, :2]
+
+        cholesky_factor = np.linalg.cholesky(covariance)
+        d = measurements - mean
+        z = scipy.linalg.solve_triangular(
+            cholesky_factor, d.T, lower=True, check_finite=False,
+            overwrite_b=True)
+        squared_maha = np.sum(z * z, axis=0)
+        return squared_maha
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/deep_sort/linear_assignment.py b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/deep_sort/linear_assignment.py
new file mode 100644
index 0000000000000000000000000000000000000000..47c8bf065bad27dd276fd6a33efc184f15a59c91
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/deep_sort/linear_assignment.py	
@@ -0,0 +1,191 @@
+# vim: expandtab:ts=4:sw=4
+from __future__ import absolute_import
+import numpy as np
+from scipy.optimize import linear_sum_assignment
+from . import kalman_filter
+
+
+INFTY_COST = 1e+5
+
+
+def min_cost_matching(
+        distance_metric, max_distance, tracks, detections, track_indices=None,
+        detection_indices=None):
+    """Solve linear assignment problem.
+
+    Parameters
+    ----------
+    distance_metric : Callable[List[Track], List[Detection], List[int], List[int]) -> ndarray
+        The distance metric is given a list of tracks and detections as well as
+        a list of N track indices and M detection indices. The metric should
+        return the NxM dimensional cost matrix, where element (i, j) is the
+        association cost between the i-th track in the given track indices and
+        the j-th detection in the given detection_indices.
+    max_distance : float
+        Gating threshold. Associations with cost larger than this value are
+        disregarded.
+    tracks : List[track.Track]
+        A list of predicted tracks at the current time step.
+    detections : List[detection.Detection]
+        A list of detections at the current time step.
+    track_indices : List[int]
+        List of track indices that maps rows in `cost_matrix` to tracks in
+        `tracks` (see description above).
+    detection_indices : List[int]
+        List of detection indices that maps columns in `cost_matrix` to
+        detections in `detections` (see description above).
+
+    Returns
+    -------
+    (List[(int, int)], List[int], List[int])
+        Returns a tuple with the following three entries:
+        * A list of matched track and detection indices.
+        * A list of unmatched track indices.
+        * A list of unmatched detection indices.
+
+    """
+    if track_indices is None:
+        track_indices = np.arange(len(tracks))
+    if detection_indices is None:
+        detection_indices = np.arange(len(detections))
+
+    if len(detection_indices) == 0 or len(track_indices) == 0:
+        return [], track_indices, detection_indices  # Nothing to match.
+
+    cost_matrix = distance_metric(
+        tracks, detections, track_indices, detection_indices)
+    cost_matrix[cost_matrix > max_distance] = max_distance + 1e-5
+    indices = linear_sum_assignment(cost_matrix)
+    indices = np.asarray(indices)
+    indices = np.transpose(indices)
+    matches, unmatched_tracks, unmatched_detections = [], [], []
+    for col, detection_idx in enumerate(detection_indices):
+        if col not in indices[:, 1]:
+            unmatched_detections.append(detection_idx)
+    for row, track_idx in enumerate(track_indices):
+        if row not in indices[:, 0]:
+            unmatched_tracks.append(track_idx)
+    for row, col in indices:
+        track_idx = track_indices[row]
+        detection_idx = detection_indices[col]
+        if cost_matrix[row, col] > max_distance:
+            unmatched_tracks.append(track_idx)
+            unmatched_detections.append(detection_idx)
+        else:
+            matches.append((track_idx, detection_idx))
+    return matches, unmatched_tracks, unmatched_detections
+
+
+def matching_cascade(
+        distance_metric, max_distance, cascade_depth, tracks, detections,
+        track_indices=None, detection_indices=None):
+    """Run matching cascade.
+
+    Parameters
+    ----------
+    distance_metric : Callable[List[Track], List[Detection], List[int], List[int]) -> ndarray
+        The distance metric is given a list of tracks and detections as well as
+        a list of N track indices and M detection indices. The metric should
+        return the NxM dimensional cost matrix, where element (i, j) is the
+        association cost between the i-th track in the given track indices and
+        the j-th detection in the given detection indices.
+    max_distance : float
+        Gating threshold. Associations with cost larger than this value are
+        disregarded.
+    cascade_depth: int
+        The cascade depth, should be se to the maximum track age.
+    tracks : List[track.Track]
+        A list of predicted tracks at the current time step.
+    detections : List[detection.Detection]
+        A list of detections at the current time step.
+    track_indices : Optional[List[int]]
+        List of track indices that maps rows in `cost_matrix` to tracks in
+        `tracks` (see description above). Defaults to all tracks.
+    detection_indices : Optional[List[int]]
+        List of detection indices that maps columns in `cost_matrix` to
+        detections in `detections` (see description above). Defaults to all
+        detections.
+
+    Returns
+    -------
+    (List[(int, int)], List[int], List[int])
+        Returns a tuple with the following three entries:
+        * A list of matched track and detection indices.
+        * A list of unmatched track indices.
+        * A list of unmatched detection indices.
+
+    """
+    if track_indices is None:
+        track_indices = list(range(len(tracks)))
+    if detection_indices is None:
+        detection_indices = list(range(len(detections)))
+
+    unmatched_detections = detection_indices
+    matches = []
+    for level in range(cascade_depth):
+        if len(unmatched_detections) == 0:  # No detections left
+            break
+
+        track_indices_l = [
+            k for k in track_indices
+            if tracks[k].time_since_update == 1 + level
+        ]
+        if len(track_indices_l) == 0:  # Nothing to match at this level
+            continue
+
+        matches_l, _, unmatched_detections = \
+            min_cost_matching(
+                distance_metric, max_distance, tracks, detections,
+                track_indices_l, unmatched_detections)
+        matches += matches_l
+    unmatched_tracks = list(set(track_indices) - set(k for k, _ in matches))
+    return matches, unmatched_tracks, unmatched_detections
+
+
+def gate_cost_matrix(
+        kf, cost_matrix, tracks, detections, track_indices, detection_indices,
+        gated_cost=INFTY_COST, only_position=False):
+    """Invalidate infeasible entries in cost matrix based on the state
+    distributions obtained by Kalman filtering.
+
+    Parameters
+    ----------
+    kf : The Kalman filter.
+    cost_matrix : ndarray
+        The NxM dimensional cost matrix, where N is the number of track indices
+        and M is the number of detection indices, such that entry (i, j) is the
+        association cost between `tracks[track_indices[i]]` and
+        `detections[detection_indices[j]]`.
+    tracks : List[track.Track]
+        A list of predicted tracks at the current time step.
+    detections : List[detection.Detection]
+        A list of detections at the current time step.
+    track_indices : List[int]
+        List of track indices that maps rows in `cost_matrix` to tracks in
+        `tracks` (see description above).
+    detection_indices : List[int]
+        List of detection indices that maps columns in `cost_matrix` to
+        detections in `detections` (see description above).
+    gated_cost : Optional[float]
+        Entries in the cost matrix corresponding to infeasible associations are
+        set this value. Defaults to a very large value.
+    only_position : Optional[bool]
+        If True, only the x, y position of the state distribution is considered
+        during gating. Defaults to False.
+
+    Returns
+    -------
+    ndarray
+        Returns the modified cost matrix.
+
+    """
+    gating_dim = 2 if only_position else 4
+    gating_threshold = kalman_filter.chi2inv95[gating_dim]
+    measurements = np.asarray(
+        [detections[i].to_xyah() for i in detection_indices])
+    for row, track_idx in enumerate(track_indices):
+        track = tracks[track_idx]
+        gating_distance = kf.gating_distance(
+            track.mean, track.covariance, measurements, only_position)
+        cost_matrix[row, gating_distance > gating_threshold] = gated_cost
+    return cost_matrix
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/deep_sort/nn_matching.py b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/deep_sort/nn_matching.py
new file mode 100644
index 0000000000000000000000000000000000000000..2e7bfea4b87b0c256274937c8323ffc93fa5d61b
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/deep_sort/nn_matching.py	
@@ -0,0 +1,177 @@
+# vim: expandtab:ts=4:sw=4
+import numpy as np
+
+
+def _pdist(a, b):
+    """Compute pair-wise squared distance between points in `a` and `b`.
+
+    Parameters
+    ----------
+    a : array_like
+        An NxM matrix of N samples of dimensionality M.
+    b : array_like
+        An LxM matrix of L samples of dimensionality M.
+
+    Returns
+    -------
+    ndarray
+        Returns a matrix of size len(a), len(b) such that eleement (i, j)
+        contains the squared distance between `a[i]` and `b[j]`.
+
+    """
+    a, b = np.asarray(a), np.asarray(b)
+    if len(a) == 0 or len(b) == 0:
+        return np.zeros((len(a), len(b)))
+    a2, b2 = np.square(a).sum(axis=1), np.square(b).sum(axis=1)
+    r2 = -2. * np.dot(a, b.T) + a2[:, None] + b2[None, :]
+    r2 = np.clip(r2, 0., float(np.inf))
+    return r2
+
+
+def _cosine_distance(a, b, data_is_normalized=False):
+    """Compute pair-wise cosine distance between points in `a` and `b`.
+
+    Parameters
+    ----------
+    a : array_like
+        An NxM matrix of N samples of dimensionality M.
+    b : array_like
+        An LxM matrix of L samples of dimensionality M.
+    data_is_normalized : Optional[bool]
+        If True, assumes rows in a and b are unit length vectors.
+        Otherwise, a and b are explicitly normalized to lenght 1.
+
+    Returns
+    -------
+    ndarray
+        Returns a matrix of size len(a), len(b) such that eleement (i, j)
+        contains the squared distance between `a[i]` and `b[j]`.
+
+    """
+    if not data_is_normalized:
+        a = np.asarray(a) / np.linalg.norm(a, axis=1, keepdims=True)
+        b = np.asarray(b) / np.linalg.norm(b, axis=1, keepdims=True)
+    return 1. - np.dot(a, b.T)
+
+
+def _nn_euclidean_distance(x, y):
+    """ Helper function for nearest neighbor distance metric (Euclidean).
+
+    Parameters
+    ----------
+    x : ndarray
+        A matrix of N row-vectors (sample points).
+    y : ndarray
+        A matrix of M row-vectors (query points).
+
+    Returns
+    -------
+    ndarray
+        A vector of length M that contains for each entry in `y` the
+        smallest Euclidean distance to a sample in `x`.
+
+    """
+    distances = _pdist(x, y)
+    return np.maximum(0.0, distances.min(axis=0))
+
+
+def _nn_cosine_distance(x, y):
+    """ Helper function for nearest neighbor distance metric (cosine).
+
+    Parameters
+    ----------
+    x : ndarray
+        A matrix of N row-vectors (sample points).
+    y : ndarray
+        A matrix of M row-vectors (query points).
+
+    Returns
+    -------
+    ndarray
+        A vector of length M that contains for each entry in `y` the
+        smallest cosine distance to a sample in `x`.
+
+    """
+    distances = _cosine_distance(x, y)
+    return distances.min(axis=0)
+
+
+class NearestNeighborDistanceMetric(object):
+    """
+    A nearest neighbor distance metric that, for each target, returns
+    the closest distance to any sample that has been observed so far.
+
+    Parameters
+    ----------
+    metric : str
+        Either "euclidean" or "cosine".
+    matching_threshold: float
+        The matching threshold. Samples with larger distance are considered an
+        invalid match.
+    budget : Optional[int]
+        If not None, fix samples per class to at most this number. Removes
+        the oldest samples when the budget is reached.
+
+    Attributes
+    ----------
+    samples : Dict[int -> List[ndarray]]
+        A dictionary that maps from target identities to the list of samples
+        that have been observed so far.
+
+    """
+
+    def __init__(self, metric, matching_threshold, budget=None):
+
+
+        if metric == "euclidean":
+            self._metric = _nn_euclidean_distance
+        elif metric == "cosine":
+            self._metric = _nn_cosine_distance
+        else:
+            raise ValueError(
+                "Invalid metric; must be either 'euclidean' or 'cosine'")
+        self.matching_threshold = matching_threshold
+        self.budget = budget
+        self.samples = {}
+
+    def partial_fit(self, features, targets, active_targets):
+        """Update the distance metric with new data.
+
+        Parameters
+        ----------
+        features : ndarray
+            An NxM matrix of N features of dimensionality M.
+        targets : ndarray
+            An integer array of associated target identities.
+        active_targets : List[int]
+            A list of targets that are currently present in the scene.
+
+        """
+        for feature, target in zip(features, targets):
+            self.samples.setdefault(target, []).append(feature)
+            if self.budget is not None:
+                self.samples[target] = self.samples[target][-self.budget:]
+        self.samples = {k: self.samples[k] for k in active_targets}
+
+    def distance(self, features, targets):
+        """Compute distance between features and targets.
+
+        Parameters
+        ----------
+        features : ndarray
+            An NxM matrix of N features of dimensionality M.
+        targets : List[int]
+            A list of targets to match the given `features` against.
+
+        Returns
+        -------
+        ndarray
+            Returns a cost matrix of shape len(targets), len(features), where
+            element (i, j) contains the closest squared distance between
+            `targets[i]` and `features[j]`.
+
+        """
+        cost_matrix = np.zeros((len(targets), len(features)))
+        for i, target in enumerate(targets):
+            cost_matrix[i, :] = self._metric(self.samples[target], features)
+        return cost_matrix
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/deep_sort/preprocessing.py b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/deep_sort/preprocessing.py
new file mode 100644
index 0000000000000000000000000000000000000000..13bc3269d194cc597f838697f3dd03f46633b41e
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/deep_sort/preprocessing.py	
@@ -0,0 +1,74 @@
+# vim: expandtab:ts=4:sw=4
+import numpy as np
+import cv2
+
+
+def non_max_suppression(boxes, classes, max_bbox_overlap, scores=None):
+    """Suppress overlapping detections.
+
+    Original code from [1]_ has been adapted to include confidence score.
+
+    .. [1] http://www.pyimagesearch.com/2015/02/16/
+           faster-non-maximum-suppression-python/
+
+    Examples
+    --------
+
+        >>> boxes = [d.roi for d in detections]
+        >>> classes = [d.classes for d in detections]
+        >>> scores = [d.confidence for d in detections]
+        >>> indices = non_max_suppression(boxes, max_bbox_overlap, scores)
+        >>> detections = [detections[i] for i in indices]
+
+    Parameters
+    ----------
+    boxes : ndarray
+        Array of ROIs (x, y, width, height).
+    max_bbox_overlap : float
+        ROIs that overlap more than this values are suppressed.
+    scores : Optional[array_like]
+        Detector confidence score.
+
+    Returns
+    -------
+    List[int]
+        Returns indices of detections that have survived non-maxima suppression.
+
+    """
+    if len(boxes) == 0:
+        return []
+
+    boxes = boxes.astype(np.float)
+    pick = []
+
+    x1 = boxes[:, 0]
+    y1 = boxes[:, 1]
+    x2 = boxes[:, 2] + boxes[:, 0]
+    y2 = boxes[:, 3] + boxes[:, 1]
+
+    area = (x2 - x1 + 1) * (y2 - y1 + 1)
+    if scores is not None:
+        idxs = np.argsort(scores)
+    else:
+        idxs = np.argsort(y2)
+
+    while len(idxs) > 0:
+        last = len(idxs) - 1
+        i = idxs[last]
+        pick.append(i)
+
+        xx1 = np.maximum(x1[i], x1[idxs[:last]])
+        yy1 = np.maximum(y1[i], y1[idxs[:last]])
+        xx2 = np.minimum(x2[i], x2[idxs[:last]])
+        yy2 = np.minimum(y2[i], y2[idxs[:last]])
+
+        w = np.maximum(0, xx2 - xx1 + 1)
+        h = np.maximum(0, yy2 - yy1 + 1)
+
+        overlap = (w * h) / area[idxs[:last]]
+
+        idxs = np.delete(
+            idxs, np.concatenate(
+                ([last], np.where(overlap > max_bbox_overlap)[0])))
+
+    return pick
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/deep_sort/test_tracking.gif b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/deep_sort/test_tracking.gif
new file mode 100644
index 0000000000000000000000000000000000000000..7fd45e80f06431235a36fb3985e88db95e49de54
Binary files /dev/null and b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/deep_sort/test_tracking.gif differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/deep_sort/track.py b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/deep_sort/track.py
new file mode 100644
index 0000000000000000000000000000000000000000..68bb88c8990e117d416950eb0912d600c2e3a8c1
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/deep_sort/track.py	
@@ -0,0 +1,170 @@
+# vim: expandtab:ts=4:sw=4
+
+
+class TrackState:
+    """
+    Enumeration type for the single target track state. Newly created tracks are
+    classified as `tentative` until enough evidence has been collected. Then,
+    the track state is changed to `confirmed`. Tracks that are no longer alive
+    are classified as `deleted` to mark them for removal from the set of active
+    tracks.
+
+    """
+
+    Tentative = 1
+    Confirmed = 2
+    Deleted = 3
+
+
+class Track:
+    """
+    A single target track with state space `(x, y, a, h)` and associated
+    velocities, where `(x, y)` is the center of the bounding box, `a` is the
+    aspect ratio and `h` is the height.
+
+    Parameters
+    ----------
+    mean : ndarray
+        Mean vector of the initial state distribution.
+    covariance : ndarray
+        Covariance matrix of the initial state distribution.
+    track_id : int
+        A unique track identifier.
+    n_init : int
+        Number of consecutive detections before the track is confirmed. The
+        track state is set to `Deleted` if a miss occurs within the first
+        `n_init` frames.
+    max_age : int
+        The maximum number of consecutive misses before the track state is
+        set to `Deleted`.
+    feature : Optional[ndarray]
+        Feature vector of the detection this track originates from. If not None,
+        this feature is added to the `features` cache.
+
+    Attributes
+    ----------
+    mean : ndarray
+        Mean vector of the initial state distribution.
+    covariance : ndarray
+        Covariance matrix of the initial state distribution.
+    track_id : int
+        A unique track identifier.
+    hits : int
+        Total number of measurement updates.
+    age : int
+        Total number of frames since first occurance.
+    time_since_update : int
+        Total number of frames since last measurement update.
+    state : TrackState
+        The current track state.
+    features : List[ndarray]
+        A cache of features. On each measurement update, the associated feature
+        vector is added to this list.
+
+    """
+
+    def __init__(self, mean, covariance, track_id, n_init, max_age,
+                 feature=None, class_name=None):
+        self.mean = mean
+        self.covariance = covariance
+        self.track_id = track_id
+        self.hits = 1
+        self.age = 1
+        self.time_since_update = 0
+
+        self.state = TrackState.Tentative
+        self.features = []
+        if feature is not None:
+            self.features.append(feature)
+
+        self._n_init = n_init
+        self._max_age = max_age
+        self.class_name = class_name
+
+    def to_tlwh(self):
+        """Get current position in bounding box format `(top left x, top left y,
+        width, height)`.
+
+        Returns
+        -------
+        ndarray
+            The bounding box.
+
+        """
+        ret = self.mean[:4].copy()
+        ret[2] *= ret[3]
+        ret[:2] -= ret[2:] / 2
+        return ret
+
+    def to_tlbr(self):
+        """Get current position in bounding box format `(min x, miny, max x,
+        max y)`.
+
+        Returns
+        -------
+        ndarray
+            The bounding box.
+
+        """
+        ret = self.to_tlwh()
+        ret[2:] = ret[:2] + ret[2:]
+        return ret
+    
+    def get_class(self):
+        return self.class_name
+
+    def predict(self, kf):
+        """Propagate the state distribution to the current time step using a
+        Kalman filter prediction step.
+
+        Parameters
+        ----------
+        kf : kalman_filter.KalmanFilter
+            The Kalman filter.
+
+        """
+        self.mean, self.covariance = kf.predict(self.mean, self.covariance)
+        self.age += 1
+        self.time_since_update += 1
+
+    def update(self, kf, detection):
+        """Perform Kalman filter measurement update step and update the feature
+        cache.
+
+        Parameters
+        ----------
+        kf : kalman_filter.KalmanFilter
+            The Kalman filter.
+        detection : Detection
+            The associated detection.
+
+        """
+        self.mean, self.covariance = kf.update(
+            self.mean, self.covariance, detection.to_xyah())
+        self.features.append(detection.feature)
+
+        self.hits += 1
+        self.time_since_update = 0
+        if self.state == TrackState.Tentative and self.hits >= self._n_init:
+            self.state = TrackState.Confirmed
+
+    def mark_missed(self):
+        """Mark this track as missed (no association at the current time step).
+        """
+        if self.state == TrackState.Tentative:
+            self.state = TrackState.Deleted
+        elif self.time_since_update > self._max_age:
+            self.state = TrackState.Deleted
+
+    def is_tentative(self):
+        """Returns True if this track is tentative (unconfirmed).
+        """
+        return self.state == TrackState.Tentative
+
+    def is_confirmed(self):
+        """Returns True if this track is confirmed."""
+        return self.state == TrackState.Confirmed
+
+    def is_deleted(self):
+        """Returns True if this track is dead and should be deleted."""
+        return self.state == TrackState.Deleted
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/deep_sort/tracker.py b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/deep_sort/tracker.py
new file mode 100644
index 0000000000000000000000000000000000000000..969e1b5447af1bed5bc295a8f6a40a754d305f97
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/deep_sort/tracker.py	
@@ -0,0 +1,139 @@
+# vim: expandtab:ts=4:sw=4
+from __future__ import absolute_import
+import numpy as np
+from . import kalman_filter
+from . import linear_assignment
+from . import iou_matching
+from .track import Track
+
+
+class Tracker:
+    """
+    This is the multi-target tracker.
+
+    Parameters
+    ----------
+    metric : nn_matching.NearestNeighborDistanceMetric
+        A distance metric for measurement-to-track association.
+    max_age : int
+        Maximum number of missed misses before a track is deleted.
+    n_init : int
+        Number of consecutive detections before the track is confirmed. The
+        track state is set to `Deleted` if a miss occurs within the first
+        `n_init` frames.
+
+    Attributes
+    ----------
+    metric : nn_matching.NearestNeighborDistanceMetric
+        The distance metric used for measurement to track association.
+    max_age : int
+        Maximum number of missed misses before a track is deleted.
+    n_init : int
+        Number of frames that a track remains in initialization phase.
+    kf : kalman_filter.KalmanFilter
+        A Kalman filter to filter target trajectories in image space.
+    tracks : List[Track]
+        The list of active tracks at the current time step.
+
+    """
+
+    def __init__(self, metric, max_iou_distance=0.7, max_age=30, n_init=3):
+        self.metric = metric
+        self.max_iou_distance = max_iou_distance
+        self.max_age = max_age
+        self.n_init = n_init
+
+        self.kf = kalman_filter.KalmanFilter()
+        self.tracks = []
+        self._next_id = 1
+
+    def predict(self):
+        """Propagate track state distributions one time step forward.
+
+        This function should be called once every time step, before `update`.
+        """
+        for track in self.tracks:
+            track.predict(self.kf)
+
+    def update(self, detections):
+        """Perform measurement update and track management.
+
+        Parameters
+        ----------
+        detections : List[deep_sort.detection.Detection]
+            A list of detections at the current time step.
+
+        """
+        # Run matching cascade.
+        matches, unmatched_tracks, unmatched_detections = \
+            self._match(detections)
+
+        # Update track set.
+        for track_idx, detection_idx in matches:
+            self.tracks[track_idx].update(
+                self.kf, detections[detection_idx])
+        for track_idx in unmatched_tracks:
+            self.tracks[track_idx].mark_missed()
+        for detection_idx in unmatched_detections:
+            self._initiate_track(detections[detection_idx])
+        self.tracks = [t for t in self.tracks if not t.is_deleted()]
+
+        # Update distance metric.
+        active_targets = [t.track_id for t in self.tracks if t.is_confirmed()]
+        features, targets = [], []
+        for track in self.tracks:
+            if not track.is_confirmed():
+                continue
+            features += track.features
+            targets += [track.track_id for _ in track.features]
+            track.features = []
+        self.metric.partial_fit(
+            np.asarray(features), np.asarray(targets), active_targets)
+
+    def _match(self, detections):
+
+        def gated_metric(tracks, dets, track_indices, detection_indices):
+            features = np.array([dets[i].feature for i in detection_indices])
+            targets = np.array([tracks[i].track_id for i in track_indices])
+            cost_matrix = self.metric.distance(features, targets)
+            cost_matrix = linear_assignment.gate_cost_matrix(
+                self.kf, cost_matrix, tracks, dets, track_indices,
+                detection_indices)
+
+            return cost_matrix
+
+        # Split track set into confirmed and unconfirmed tracks.
+        confirmed_tracks = [
+            i for i, t in enumerate(self.tracks) if t.is_confirmed()]
+        unconfirmed_tracks = [
+            i for i, t in enumerate(self.tracks) if not t.is_confirmed()]
+
+        # Associate confirmed tracks using appearance features.
+        matches_a, unmatched_tracks_a, unmatched_detections = \
+            linear_assignment.matching_cascade(
+                gated_metric, self.metric.matching_threshold, self.max_age,
+                self.tracks, detections, confirmed_tracks)
+
+        # Associate remaining tracks together with unconfirmed tracks using IOU.
+        iou_track_candidates = unconfirmed_tracks + [
+            k for k in unmatched_tracks_a if
+            self.tracks[k].time_since_update == 1]
+        unmatched_tracks_a = [
+            k for k in unmatched_tracks_a if
+            self.tracks[k].time_since_update != 1]
+        matches_b, unmatched_tracks_b, unmatched_detections = \
+            linear_assignment.min_cost_matching(
+                iou_matching.iou_cost, self.max_iou_distance, self.tracks,
+                detections, iou_track_candidates, unmatched_detections)
+
+        matches = matches_a + matches_b
+        unmatched_tracks = list(set(unmatched_tracks_a + unmatched_tracks_b))
+        return matches, unmatched_tracks, unmatched_detections
+
+    def _initiate_track(self, detection):
+        mean, covariance = self.kf.initiate(detection.to_xyah())
+        class_name = detection.get_class()
+        self.tracks.append(Track(
+            mean, covariance, self._next_id, self.n_init, self.max_age,
+            detection.feature, class_name))
+        self._next_id += 1
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/detect_mnist.py b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/detect_mnist.py
new file mode 100644
index 0000000000000000000000000000000000000000..3deafec505d189afedfdea57576afddaeaf46269
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/detect_mnist.py	
@@ -0,0 +1,32 @@
+#================================================================
+#
+#   File name   : detect_mnist.py
+#   Author      : PyLessons
+#   Created date: 2020-08-12
+#   Website     : https://pylessons.com/
+#   GitHub      : https://github.com/pythonlessons/TensorFlow-2.x-YOLOv3
+#   Description : mnist object detection example
+#
+#================================================================
+import os
+os.environ['CUDA_VISIBLE_DEVICES'] = '0'
+import cv2
+import numpy as np
+import random
+import time
+import tensorflow as tf
+from yolov3.yolov4 import Create_Yolo
+from yolov3.utils import detect_image
+from yolov3.configs import *
+
+while True:
+    ID = random.randint(0, 200)
+    label_txt = "mnist/mnist_test.txt"
+    image_info = open(label_txt).readlines()[ID].split()
+
+    image_path = image_info[0]
+
+    yolo = Create_Yolo(input_size=YOLO_INPUT_SIZE, CLASSES=TRAIN_CLASSES)
+    yolo.load_weights(f"./checkpoints/{TRAIN_MODEL_NAME}") # use keras weights
+
+    detect_image(yolo, image_path, "mnist_test.jpg", input_size=YOLO_INPUT_SIZE, show=True, CLASSES=TRAIN_CLASSES, rectangle_colors=(255,0,0))
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/detection_custom.py b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/detection_custom.py
new file mode 100644
index 0000000000000000000000000000000000000000..567c150b6e59ce6cbd50973c54aec9d1e705deb1
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/detection_custom.py	
@@ -0,0 +1,27 @@
+#================================================================
+#
+#   File name   : detection_custom.py
+#   Author      : PyLessons
+#   Created date: 2020-09-17
+#   Website     : https://pylessons.com/
+#   GitHub      : https://github.com/pythonlessons/TensorFlow-2.x-YOLOv3
+#   Description : object detection image and video example
+#
+#================================================================
+import os
+os.environ['CUDA_VISIBLE_DEVICES'] = '0'
+import cv2
+import numpy as np
+import tensorflow as tf
+from yolov3.utils import detect_image, detect_realtime, detect_video, Load_Yolo_model, detect_video_realtime_mp
+from yolov3.configs import *
+
+image_path   = "./IMAGES/cone.jpg"
+video_path   = "./IMAGES/test.mp4"
+
+yolo = Load_Yolo_model()
+detect_image(yolo, image_path, "./IMAGES/cone_detect.jpg", input_size=YOLO_INPUT_SIZE, show=True, CLASSES=TRAIN_CLASSES, rectangle_colors=(255,0,0))
+#detect_video(yolo, video_path, './IMAGES/detected.mp4', input_size=YOLO_INPUT_SIZE, show=False, CLASSES=TRAIN_CLASSES, rectangle_colors=(255,0,0))
+#detect_realtime(yolo, '', input_size=YOLO_INPUT_SIZE, show=True, CLASSES=TRAIN_CLASSES, rectangle_colors=(255, 0, 0))
+
+#detect_video_realtime_mp(video_path, "Output.mp4", input_size=YOLO_INPUT_SIZE, show=True, CLASSES=TRAIN_CLASSES, rectangle_colors=(255,0,0), realtime=False)
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/detection_demo.py b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/detection_demo.py
new file mode 100644
index 0000000000000000000000000000000000000000..ffe17d4d9f22c2ff11f8b17a953c3b045d59cbf2
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/detection_demo.py	
@@ -0,0 +1,27 @@
+#================================================================
+#
+#   File name   : detection_demo.py
+#   Author      : PyLessons
+#   Created date: 2020-09-27
+#   Website     : https://pylessons.com/
+#   GitHub      : https://github.com/pythonlessons/TensorFlow-2.x-YOLOv3
+#   Description : object detection image and video example
+#
+#================================================================
+import os
+os.environ['CUDA_VISIBLE_DEVICES'] = '0'
+import cv2
+import numpy as np
+import tensorflow as tf
+from yolov3.utils import detect_image, detect_realtime, detect_video, Load_Yolo_model, detect_video_realtime_mp
+from yolov3.configs import *
+
+image_path   = "./IMAGES/kite.jpg"
+video_path   = "./IMAGES/test.mp4"
+
+yolo = Load_Yolo_model()
+#detect_image(yolo, image_path, "./IMAGES/kite_pred.jpg", input_size=YOLO_INPUT_SIZE, show=True, rectangle_colors=(255,0,0))
+#detect_video(yolo, video_path, "", input_size=YOLO_INPUT_SIZE, show=False, rectangle_colors=(255,0,0))
+detect_realtime(yolo, '', input_size=YOLO_INPUT_SIZE, show=True, rectangle_colors=(255, 0, 0))
+
+#detect_video_realtime_mp(video_path, "Output.mp4", input_size=YOLO_INPUT_SIZE, show=False, rectangle_colors=(255,0,0), realtime=False)
diff --git a/PAR 152/Yolo Tensorflow/evaluate_mAP.py b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/evaluate_mAP.py
similarity index 93%
rename from PAR 152/Yolo Tensorflow/evaluate_mAP.py
rename to PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/evaluate_mAP.py
index 0acd35388649fcf0f0c27fb8df94ff6096a06ae2..a5643c538db1680cb823ccb24003b27730f1721b 100644
--- a/PAR 152/Yolo Tensorflow/evaluate_mAP.py	
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/evaluate_mAP.py	
@@ -1,5 +1,15 @@
+#================================================================
+#
+#   File name   : evaluate_mAP.py
+#   Author      : PyLessons
+#   Created date: 2020-08-17
+#   Website     : https://pylessons.com/
+#   GitHub      : https://github.com/pythonlessons/TensorFlow-2.x-YOLOv3
+#   Description : used to evaluate model mAP and FPS
+#
+#================================================================
 import os
-os.environ['CUDA_VISIBLE_DEVICES'] = '1'
+os.environ['CUDA_VISIBLE_DEVICES'] = '0'
 import cv2
 import numpy as np
 import tensorflow as tf
@@ -127,7 +137,10 @@ def get_mAP(Yolo, dataset, score_threshold=0.25, iou_threshold=0.50, TEST_INPUT_
 
         t1 = time.time()
         if YOLO_FRAMEWORK == "tf":
-            pred_bbox = Yolo.predict(image_data)
+            if tf.__version__ > '2.4.0':
+                pred_bbox = Yolo(image_data)
+            else:
+                pred_bbox = Yolo.predict(image_data)
         elif YOLO_FRAMEWORK == "trt":
             batched_input = tf.constant(image_data)
             result = Yolo(batched_input)
@@ -276,10 +289,10 @@ if __name__ == '__main__':
             load_yolo_weights(yolo, Darknet_weights) # use Darknet weights
         else:
             yolo = Create_Yolo(input_size=YOLO_INPUT_SIZE, CLASSES=TRAIN_CLASSES)
-            yolo.load_weights(YOLO_CUSTOM_WEIGHTS) # use custom weights
+            yolo.load_weights(f"./checkpoints/{TRAIN_MODEL_NAME}") # use custom weights
         
     elif YOLO_FRAMEWORK == "trt": # TensorRT detection
-        saved_model_loaded = tf.saved_model.load(YOLO_CUSTOM_WEIGHTS, tags=[tag_constants.SERVING])
+        saved_model_loaded = tf.saved_model.load(f"./checkpoints/{TRAIN_MODEL_NAME}", tags=[tag_constants.SERVING])
         signature_keys = list(saved_model_loaded.signatures.keys())
         yolo = saved_model_loaded.signatures['serving_default']
 
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/log/events.out.tfevents.1670093324.PC-ANTOINE.14216.5.v2 b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/log/events.out.tfevents.1670093324.PC-ANTOINE.14216.5.v2
new file mode 100644
index 0000000000000000000000000000000000000000..3248506ee83f202bda19f41a35a93c118f534f74
Binary files /dev/null and b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/log/events.out.tfevents.1670093324.PC-ANTOINE.14216.5.v2 differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/log/events.out.tfevents.1670093337.PC-ANTOINE.14216.21163.v2 b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/log/events.out.tfevents.1670093337.PC-ANTOINE.14216.21163.v2
new file mode 100644
index 0000000000000000000000000000000000000000..a3dbf3d2ecf2f194ade73b8169e53046f4f9ccbd
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/0_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/0_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..50526365f15152e8a19e6b1b6d607f5a8a9178c4
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/0_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "17 264 120 460", "used": true}, {"class_name": "cone", "bbox": "4 251 55 385", "used": true}, {"class_name": "cone", "bbox": "208 262 314 460", "used": true}, {"class_name": "cone", "bbox": "269 244 338 379", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/100_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/100_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..23b343bb3098f1813746f798f8490eb59e8ba69b
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/100_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "117 168 303 293", "used": true}, {"class_name": "cone", "bbox": "349 90 492 297", "used": true}, {"class_name": "cone", "bbox": "283 100 365 259", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/101_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/101_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..082fba248a09467da9044df1f7727dc3ad750b0e
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/101_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "209 30 315 226", "used": true}, {"class_name": "cone", "bbox": "25 1 113 125", "used": true}, {"class_name": "cone", "bbox": "83 1 163 81", "used": true}, {"class_name": "cone", "bbox": "262 114 410 338", "used": true}, {"class_name": "cone", "bbox": "270 1 332 124", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/102_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/102_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..58d7404b147d2c7c552d922d25d262076583e58c
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/102_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "130 259 171 321", "used": true}, {"class_name": "cone", "bbox": "165 232 199 282", "used": true}, {"class_name": "cone", "bbox": "189 213 216 255", "used": true}, {"class_name": "cone", "bbox": "206 200 230 237", "used": true}, {"class_name": "cone", "bbox": "218 190 239 221", "used": true}, {"class_name": "cone", "bbox": "230 183 246 211", "used": true}, {"class_name": "cone", "bbox": "237 178 254 201", "used": true}, {"class_name": "cone", "bbox": "243 172 259 193", "used": true}, {"class_name": "cone", "bbox": "256 162 266 181", "used": true}, {"class_name": "cone", "bbox": "85 306 113 338", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/103_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/103_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..08c4494f8b3bbb9ad02cfc5f50af979f68fce230
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/103_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "24 58 318 471", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/104_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/104_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..e4fb8c091a1d73bce3270b95e403454363d7fcee
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/104_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "256 264 308 347", "used": true}, {"class_name": "cone", "bbox": "115 249 154 317", "used": true}, {"class_name": "cone", "bbox": "15 240 53 293", "used": true}, {"class_name": "cone", "bbox": "507 283 573 391", "used": true}, {"class_name": "cone", "bbox": "347 192 359 209", "used": true}, {"class_name": "cone", "bbox": "289 190 302 209", "used": true}, {"class_name": "cone", "bbox": "102 194 111 206", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/105_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/105_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..2660e429f6cb705307a129c406750f2a570795c1
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/105_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "6 20 367 434", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/106_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/106_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..509b7d3ba612d76ad397cc37f8f2a592e0a63008
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/106_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "206 125 321 315", "used": true}, {"class_name": "cone", "bbox": "41 126 139 322", "used": true}, {"class_name": "cone", "bbox": "154 89 233 213", "used": true}, {"class_name": "cone", "bbox": "25 89 74 213", "used": true}, {"class_name": "cone", "bbox": "71 50 119 128", "used": true}, {"class_name": "cone", "bbox": "1 45 53 119", "used": true}, {"class_name": "cone", "bbox": "394 121 508 308", "used": true}, {"class_name": "cone", "bbox": "467 88 508 202", "used": true}, {"class_name": "cone", "bbox": "346 75 413 182", "used": true}, {"class_name": "cone", "bbox": "269 66 320 162", "used": true}, {"class_name": "cone", "bbox": "199 62 252 148", "used": true}, {"class_name": "cone", "bbox": "136 55 191 136", "used": true}, {"class_name": "cone", "bbox": "109 19 136 69", "used": true}, {"class_name": "cone", "bbox": "65 18 91 69", "used": true}, {"class_name": "cone", "bbox": "24 19 52 69", "used": true}, {"class_name": "cone", "bbox": "227 22 256 72", "used": true}, {"class_name": "cone", "bbox": "185 20 213 73", "used": true}, {"class_name": "cone", "bbox": "367 24 396 77", "used": true}, {"class_name": "cone", "bbox": "413 23 448 77", "used": true}, {"class_name": "cone", "bbox": "468 23 501 80", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/107_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/107_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..1fe56446dbc94dc8ec929827abfe9ea5da4272dd
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/107_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "220 203 258 250", "used": true}, {"class_name": "cone", "bbox": "121 213 135 241", "used": true}, {"class_name": "cone", "bbox": "70 203 103 244", "used": true}, {"class_name": "cone", "bbox": "6 213 30 243", "used": true}, {"class_name": "cone", "bbox": "308 177 359 261", "used": true}, {"class_name": "cone", "bbox": "396 221 415 241", "used": true}, {"class_name": "cone", "bbox": "458 224 470 241", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/108_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/108_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..7dbe25055e1212f230093d442cdf8642d411d441
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/108_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "106 88 241 316", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/109_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/109_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..82334d0d9492fda429e72d20daa37047fdff3014
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/109_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "390 259 632 417", "used": true}, {"class_name": "cone", "bbox": "806 256 895 361", "used": true}, {"class_name": "cone", "bbox": "1 41 261 561", "used": true}, {"class_name": "cone", "bbox": "250 181 393 430", "used": true}, {"class_name": "cone", "bbox": "711 251 788 365", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/10_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/10_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..42d5d5d945e72d82af7710e38312274178b3d008
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/10_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "34 65 319 474", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/110_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/110_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..e08567a3269b202661e28d0caf0b1057a853ab53
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/110_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "169 54 228 131", "used": true}, {"class_name": "cone", "bbox": "109 51 172 134", "used": true}, {"class_name": "cone", "bbox": "26 52 91 131", "used": true}, {"class_name": "cone", "bbox": "280 51 334 127", "used": true}, {"class_name": "cone", "bbox": "351 52 398 126", "used": true}, {"class_name": "cone", "bbox": "404 50 452 128", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/111_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/111_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..0926e75bfa19acdf88993ecec81e2d5e0c69c4a5
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/111_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "83 170 169 302", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/112_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/112_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..a2eb3db1d052821354b5d60df6027788e684e92a
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/112_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "74 76 171 260", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/113_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/113_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..9b6f40ff0483c75e71b9f0886e070e3c9d421ec6
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/113_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "159 223 287 454", "used": true}, {"class_name": "cone", "bbox": "39 40 77 91", "used": true}, {"class_name": "cone", "bbox": "114 5 143 43", "used": true}, {"class_name": "cone", "bbox": "165 4 178 25", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/114_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/114_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..dfed061affd95e558423f54ae468c27cbeb9f1f0
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/114_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "148 27 262 161", "used": true}, {"class_name": "cone", "bbox": "257 1 350 137", "used": true}, {"class_name": "cone", "bbox": "1 1 122 88", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/115_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/115_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..38f578fddec63f9e864ea48f68963c963cf2ae70
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/115_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "53 47 259 456", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/116_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/116_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..442d2b479d5504e6a2eb9314aff1980cde4d64ef
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/116_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "181 298 231 389", "used": true}, {"class_name": "cone", "bbox": "205 242 243 316", "used": true}, {"class_name": "cone", "bbox": "247 205 279 266", "used": true}, {"class_name": "cone", "bbox": "258 172 286 219", "used": true}, {"class_name": "cone", "bbox": "244 147 265 182", "used": true}, {"class_name": "cone", "bbox": "198 128 222 158", "used": true}, {"class_name": "cone", "bbox": "145 129 162 162", "used": true}, {"class_name": "cone", "bbox": "100 152 123 194", "used": true}, {"class_name": "cone", "bbox": "84 180 112 234", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/117_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/117_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..2cadf691ebaf2dca3fa0b68f2f6666325e52a7ee
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/117_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "183 166 314 388", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/118_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/118_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..a5675c50d2b922304d0fa30a1d4c558062e57eeb
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/118_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "193 12 457 275", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/119_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/119_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..0e1c502a1b58ae7e561072b278fdb474add22a8b
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/119_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "52 16 354 393", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/11_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/11_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..72fd4794a30a66872c363fbfdd218ef0fb2aafdb
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/11_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "273 421 471 901", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/120_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/120_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..9717dba7a420edc27e6e9f59d5e78c465e105e18
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/120_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "60 36 291 457", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/121_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/121_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..5c5b7cd249ee2d9ce25b35fc6d02c41034d43c2d
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/121_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "268 113 315 171", "used": true}, {"class_name": "cone", "bbox": "185 113 233 170", "used": true}, {"class_name": "cone", "bbox": "103 114 150 170", "used": true}, {"class_name": "cone", "bbox": "15 114 65 170", "used": true}, {"class_name": "cone", "bbox": "351 114 402 171", "used": true}, {"class_name": "cone", "bbox": "440 114 487 171", "used": true}, {"class_name": "cone", "bbox": "528 116 575 171", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/122_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/122_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..6a375b0e2a4230663a976d6b30fc5e2635b55abf
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/122_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "63 36 341 374", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/123_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/123_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..10a6c4e8056aa325a2286a6f5c2b72216a315526
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/123_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "27 44 212 283", "used": true}, {"class_name": "cone", "bbox": "369 9 589 292", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/124_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/124_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..ea68948af6b63a56f3770d48f221c5e2da3965fd
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/124_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "50 1 183 188", "used": true}, {"class_name": "cone", "bbox": "253 64 366 268", "used": true}, {"class_name": "cone", "bbox": "339 7 429 179", "used": true}, {"class_name": "cone", "bbox": "127 99 259 335", "used": true}, {"class_name": "cone", "bbox": "425 4 509 171", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/125_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/125_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..95fcc0d564ed28a6cfb999fcde73db54895515db
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/125_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "78 55 260 308", "used": true}, {"class_name": "cone", "bbox": "257 121 356 272", "used": true}, {"class_name": "cone", "bbox": "233 180 278 247", "used": true}, {"class_name": "cone", "bbox": "43 160 123 253", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/126_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/126_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..a5b8c69163b16dfffccdbe749eb8dde1b53c58a0
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/126_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "8 1 378 441", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/127_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/127_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..d26076cb95dde372f250abf1481938652b2e908b
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/127_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "12 31 317 477", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/128_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/128_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..bd0b0b2d2be6d7d1d019b3313b8221307a4bdfa0
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/128_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "206 140 336 413", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/129_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/129_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..b94d00f925c25ec423d23a1d2b0a1e53e95b9a89
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/129_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "197 101 344 293", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/12_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/12_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..ccad0fcf34d8ac7a1c6b40822fb1a040c94dbe1b
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/12_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "177 125 421 697", "used": true}, {"class_name": "cone", "bbox": "337 269 602 1023", "used": true}, {"class_name": "cone", "bbox": "168 105 316 464", "used": true}, {"class_name": "cone", "bbox": "166 115 247 343", "used": true}, {"class_name": "cone", "bbox": "48 74 124 207", "used": true}, {"class_name": "cone", "bbox": "1 73 45 179", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/130_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/130_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..460f85329bc5bf1d67bfb0156cf3fe69620e844e
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/130_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "43 39 257 301", "used": true}, {"class_name": "cone", "bbox": "189 115 384 268", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/131_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/131_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..406c0e528d3356e9704225a57051cc3a06b74f68
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/131_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "132 37 275 302", "used": true}, {"class_name": "cone", "bbox": "2 283 99 508", "used": true}, {"class_name": "cone", "bbox": "107 1 187 95", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/132_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/132_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..29243ef2ed6ad7d1073e0e22c6d156170c096b7a
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/132_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "1 199 111 353", "used": true}, {"class_name": "cone", "bbox": "87 238 158 346", "used": true}, {"class_name": "cone", "bbox": "138 252 184 340", "used": true}, {"class_name": "cone", "bbox": "162 267 204 338", "used": true}, {"class_name": "cone", "bbox": "187 275 211 337", "used": true}, {"class_name": "cone", "bbox": "210 287 225 335", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/133_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/133_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..ddc9336c2849b4cd51a43b30d845a9feeb9eed66
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/133_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "306 52 449 189", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/134_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/134_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..9d7b9bdcb15b4df788c7e7c2fb8f5375974cda37
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/134_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "360 41 411 120", "used": true}, {"class_name": "cone", "bbox": "473 32 507 119", "used": true}, {"class_name": "cone", "bbox": "306 37 350 107", "used": true}, {"class_name": "cone", "bbox": "318 2 356 59", "used": true}, {"class_name": "cone", "bbox": "401 1 436 27", "used": true}, {"class_name": "cone", "bbox": "296 22 312 88", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/135_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/135_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..f88b7181a0bf00ceafbb88878540ebb008389ddf
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/135_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "133 102 235 257", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/136_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/136_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..f6b47756f591ac47491aa4b8f610d17e6bf4bd2d
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/136_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "49 45 260 459", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/137_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/137_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..9d1d5f9b74d5c09d3231e749daf27c8286c9da68
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/137_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "273 45 442 337", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/138_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/138_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..a8304ac8767f296ed606bbd81bcefe66a390bd7d
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/138_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "52 388 93 495", "used": true}, {"class_name": "cone", "bbox": "102 365 181 496", "used": true}, {"class_name": "cone", "bbox": "191 370 248 498", "used": true}, {"class_name": "cone", "bbox": "240 381 307 501", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/139_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/139_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..d24c3b74accaa45dfb447af3b13eb6f0363c813e
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/139_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "50 148 195 337", "used": true}, {"class_name": "cone", "bbox": "212 109 317 235", "used": true}, {"class_name": "cone", "bbox": "306 81 389 182", "used": true}, {"class_name": "cone", "bbox": "368 67 434 149", "used": true}, {"class_name": "cone", "bbox": "422 54 468 125", "used": true}, {"class_name": "cone", "bbox": "454 48 482 105", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/13_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/13_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..71a9a50eb9fb91ceef0ef10aba881e63efb84be4
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/13_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "84 150 175 337", "used": true}, {"class_name": "cone", "bbox": "214 117 233 153", "used": true}, {"class_name": "cone", "bbox": "281 121 314 174", "used": true}, {"class_name": "cone", "bbox": "259 117 283 159", "used": true}, {"class_name": "cone", "bbox": "235 118 252 151", "used": true}, {"class_name": "cone", "bbox": "186 117 199 137", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/140_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/140_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..a29d52fd41b8d4795b9892f06aeb1940b897837c
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/140_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "28 30 240 302", "used": true}, {"class_name": "cone", "bbox": "178 99 452 312", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/141_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/141_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..3a61509bb4c5e9fe88fbda9795fab3dba943dfc1
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/141_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "112 35 309 327", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/142_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/142_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..c08affe76076d61fbda09e47611a47caee283943
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/142_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "66 11 293 492", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/143_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/143_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..2d0153a79c82f0e37f6a84b1d7da16e9e72b4aff
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/143_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "45 128 240 453", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/144_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/144_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..0e210c8f8335b191e983eb637c154b49936e1e04
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/144_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "69 52 296 462", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/145_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/145_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..fc42eaa871d22e2f962865e559bf13d7128a8ca3
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/145_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "154 205 284 423", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/146_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/146_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..2ccd72c0cc91875a2e2aefd9c458a58ef485118c
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/146_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "175 42 292 277", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/147_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/147_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..b06c582e98886f2f857e2e7a4e7415e078d70f6c
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/147_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "124 127 228 296", "used": true}, {"class_name": "cone", "bbox": "6 69 93 194", "used": true}, {"class_name": "cone", "bbox": "255 36 321 145", "used": true}, {"class_name": "cone", "bbox": "423 81 519 223", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/148_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/148_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..3f07161e0c0e52178aea04d016739271984887d5
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/148_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "184 63 289 256", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/149_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/149_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..799060748724bf8d9c06516ca7a086b4d8bd9a02
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/149_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "314 202 386 318", "used": true}, {"class_name": "cone", "bbox": "93 207 158 313", "used": true}, {"class_name": "cone", "bbox": "403 217 446 285", "used": true}, {"class_name": "cone", "bbox": "276 215 322 291", "used": true}, {"class_name": "cone", "bbox": "17 223 47 273", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/14_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/14_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..a2bf6921b453192266b742edb1532bb4d17e2ac1
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/14_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "96 64 232 283", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/150_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/150_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..012a54a5626ff76e95ca34e05bedd90b0354aad0
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/150_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "273 32 396 243", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/151_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/151_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..da098357ca244565a77556fa3ead8cbb28c3e4aa
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/151_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "3 37 333 478", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/152_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/152_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..9e104a71e1c48309ab84cb8ea6fc9935e86f3289
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/152_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "109 68 169 174", "used": true}, {"class_name": "cone", "bbox": "215 181 340 302", "used": true}, {"class_name": "cone", "bbox": "135 141 224 244", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/153_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/153_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..6d65eb5bc3387c8275934f62281d84183cbc6e73
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/153_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "118 113 338 423", "used": true}, {"class_name": "cone", "bbox": "95 175 190 313", "used": true}, {"class_name": "cone", "bbox": "78 196 120 283", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/154_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/154_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..be4d1ef7e999457390c41f1d167babbca6fa720b
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/154_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "6 39 332 468", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/155_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/155_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..441f82d29c53116f85189f4f99dbf7d7e9e70e53
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/155_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "29 108 423 309", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/156_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/156_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..5d91115139de40f2cf28fa7315ce77aab9b8df14
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/156_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "73 232 155 405", "used": true}, {"class_name": "cone", "bbox": "247 72 323 223", "used": true}, {"class_name": "cone", "bbox": "188 111 251 269", "used": true}, {"class_name": "cone", "bbox": "126 166 203 330", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/157_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/157_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..73813f380bb77719d7bdd374845c715b49408854
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/157_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "242 383 267 436", "used": true}, {"class_name": "cone", "bbox": "171 377 213 437", "used": true}, {"class_name": "cone", "bbox": "107 381 143 434", "used": true}, {"class_name": "cone", "bbox": "32 378 73 436", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/158_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/158_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..10c6afb22b45f189a7da2bbb5f55222e1e8beadf
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/158_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "156 83 255 278", "used": true}, {"class_name": "cone", "bbox": "82 36 170 206", "used": true}, {"class_name": "cone", "bbox": "256 74 359 251", "used": true}, {"class_name": "cone", "bbox": "158 14 241 160", "used": true}, {"class_name": "cone", "bbox": "246 28 338 175", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/159_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/159_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..c32dd24706cb1a7e1e9e0a0d7498dd0eac68b876
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/159_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "1 1 97 103", "used": true}, {"class_name": "cone", "bbox": "35 16 161 104", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/15_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/15_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..98f76c09e9ef82f3cb946be2186911027300eecf
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/15_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "1 275 68 362", "used": true}, {"class_name": "cone", "bbox": "72 286 124 358", "used": true}, {"class_name": "cone", "bbox": "123 294 164 354", "used": true}, {"class_name": "cone", "bbox": "159 299 193 354", "used": true}, {"class_name": "cone", "bbox": "190 302 215 354", "used": true}, {"class_name": "cone", "bbox": "208 306 232 354", "used": true}, {"class_name": "cone", "bbox": "227 312 249 350", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/160_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/160_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..2b1cf21623917fa36e05c5df13c919479d5f1175
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/160_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "125 259 225 397", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/161_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/161_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..10125b41bfd980b1cc70291e73d5e35a7de1a626
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/161_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "251 152 302 235", "used": true}, {"class_name": "cone", "bbox": "246 149 272 209", "used": true}, {"class_name": "cone", "bbox": "242 145 258 184", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/162_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/162_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..f29a22e449d40765a2b7681bd767e777b1d35b35
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/162_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "24 51 223 387", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/163_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/163_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..8deeb2d7d55d6146d9a50e6c713f74dcb4600bbf
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/163_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "138 164 306 457", "used": true}, {"class_name": "cone", "bbox": "53 162 109 252", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/164_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/164_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..589b85aa8499910f9addd48a2c2e4ad6a3e5d5ec
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/164_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "126 267 139 285", "used": true}, {"class_name": "cone", "bbox": "391 261 402 282", "used": true}, {"class_name": "cone", "bbox": "264 260 277 281", "used": true}, {"class_name": "cone", "bbox": "194 261 208 282", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/165_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/165_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..fb3008ea3a9b8b8b3cbf2c69ef509e3f17cea2ae
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/165_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "19 32 261 309", "used": true}, {"class_name": "cone", "bbox": "268 76 438 275", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/166_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/166_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..488d46bbeb60a8afde717f42a8c3e6b4ceeab8f8
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/166_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "249 182 346 303", "used": true}, {"class_name": "cone", "bbox": "107 189 223 272", "used": true}, {"class_name": "cone", "bbox": "48 155 128 259", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/167_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/167_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..18f49515499e11f2ce7a7342d7d2816e901aa218
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/167_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "143 48 305 280", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/168_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/168_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..5c21d136655c55d4817219bf5a26823232e3a7bf
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/168_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "1 46 246 655", "used": false}, {"class_name": "cone", "bbox": "1 42 265 562", "used": true}, {"class_name": "cone", "bbox": "431 225 537 393", "used": true}, {"class_name": "cone", "bbox": "652 244 737 371", "used": true}, {"class_name": "cone", "bbox": "813 260 894 362", "used": true}, {"class_name": "cone", "bbox": "316 197 389 432", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/169_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/169_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..991c05c2daeec0f6233f3997d8067c0cfabe3323
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/169_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "68 52 299 467", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/16_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/16_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..3ebcffd3ea3d0e900ca8cb60016a4d75cc6d9053
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/16_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "201 144 331 406", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/170_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/170_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..526f7eac418c8e035c1011f20e19aa2f906f1e30
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/170_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "1 245 124 430", "used": true}, {"class_name": "cone", "bbox": "115 102 210 203", "used": true}, {"class_name": "cone", "bbox": "175 41 232 110", "used": true}, {"class_name": "cone", "bbox": "231 15 278 74", "used": true}, {"class_name": "cone", "bbox": "267 1 309 39", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/171_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/171_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..8897498f56033272230b26c16fa9d402660655b0
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/171_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "121 115 340 426", "used": true}, {"class_name": "cone", "bbox": "104 175 192 314", "used": true}, {"class_name": "cone", "bbox": "74 196 118 289", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/172_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/172_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..d82f23f22c5e361e870fc79ea155632c602baf47
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/172_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "202 92 249 178", "used": true}, {"class_name": "cone", "bbox": "225 182 419 321", "used": true}, {"class_name": "cone", "bbox": "323 111 370 167", "used": true}, {"class_name": "cone", "bbox": "265 94 286 138", "used": true}, {"class_name": "cone", "bbox": "230 109 247 134", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/173_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/173_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..72903394e67fa21c513573f450546d253a6df886
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/173_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "274 57 391 196", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/174_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/174_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..823788a64654592dc90e49a8431752fae611076a
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/174_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "309 158 383 264", "used": true}, {"class_name": "cone", "bbox": "13 161 85 264", "used": true}, {"class_name": "cone", "bbox": "34 90 95 166", "used": true}, {"class_name": "cone", "bbox": "276 82 324 156", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/175_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/175_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..944671c4da1b369bff1939c8e31cb6bbf37c610d
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/175_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "104 218 263 435", "used": true}, {"class_name": "cone", "bbox": "54 30 259 190", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/176_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/176_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..0d823701739b3386fb7c8e2b2bea381405d3fcce
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/176_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "132 162 304 478", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/177_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/177_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..4704c0caf0e09c61a8ebceb63bb71957010ad5af
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/177_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "65 9 343 399", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/178_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/178_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..c68908a839c48664a0fb613e061f25dc7f864776
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/178_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "145 377 186 445", "used": true}, {"class_name": "cone", "bbox": "251 357 263 392", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/179_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/179_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..972d232845763b8d438a3222aafeef69ef502f40
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/179_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "49 218 81 262", "used": true}, {"class_name": "cone", "bbox": "146 218 174 260", "used": true}, {"class_name": "cone", "bbox": "182 218 210 259", "used": true}, {"class_name": "cone", "bbox": "210 220 230 257", "used": true}, {"class_name": "cone", "bbox": "230 218 253 256", "used": true}, {"class_name": "cone", "bbox": "301 218 326 255", "used": true}, {"class_name": "cone", "bbox": "443 217 474 250", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/17_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/17_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..f27062dc6d95aae7951978f864f5f786a9afce71
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/17_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "99 60 194 234", "used": true}, {"class_name": "cone", "bbox": "279 51 398 303", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/180_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/180_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..2927cf1267a706e3cdc8941a34bce467bd592609
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/180_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "312 168 402 325", "used": true}, {"class_name": "cone", "bbox": "430 23 462 75", "used": true}, {"class_name": "cone", "bbox": "366 22 399 77", "used": true}, {"class_name": "cone", "bbox": "311 23 341 75", "used": true}, {"class_name": "cone", "bbox": "292 117 366 248", "used": true}, {"class_name": "cone", "bbox": "280 92 333 202", "used": true}, {"class_name": "cone", "bbox": "121 16 155 65", "used": true}, {"class_name": "cone", "bbox": "65 16 106 66", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/181_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/181_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..547fbe37a352789af10a5a06668b79ad82f1acb2
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/181_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "53 57 292 434", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/182_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/182_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..97dfad7a7ab9c49ade803aca9748af7a47657703
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/182_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "40 126 240 450", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/183_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/183_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..53ccfb89cf1302a09c358fdb88236ca4e883e798
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/183_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "1 57 182 569", "used": true}, {"class_name": "cone", "bbox": "44 53 237 479", "used": true}, {"class_name": "cone", "bbox": "133 66 296 409", "used": true}, {"class_name": "cone", "bbox": "204 62 333 364", "used": true}, {"class_name": "cone", "bbox": "277 43 397 315", "used": true}, {"class_name": "cone", "bbox": "330 33 429 275", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/184_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/184_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..e0e47f20a2df5062bd91ee90699d6c31a7892cd7
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/184_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "400 20 508 255", "used": true}, {"class_name": "cone", "bbox": "294 19 405 205", "used": true}, {"class_name": "cone", "bbox": "226 18 312 172", "used": true}, {"class_name": "cone", "bbox": "164 15 243 148", "used": true}, {"class_name": "cone", "bbox": "107 12 172 125", "used": true}, {"class_name": "cone", "bbox": "84 13 135 114", "used": true}, {"class_name": "cone", "bbox": "58 11 99 102", "used": true}, {"class_name": "cone", "bbox": "310 7 341 64", "used": true}, {"class_name": "cone", "bbox": "423 8 464 79", "used": true}, {"class_name": "cone", "bbox": "171 8 194 46", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/185_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/185_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..d55b54dac5b375359986737e9d41bd12eebfa863
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/185_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "85 16 159 147", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/186_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/186_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..adffb156040fb3d235825b5b07ab564486ebbc94
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/186_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "296 184 408 319", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/187_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/187_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..37edfc6212bad1746bffd7d7e2bfc4d0217c49ae
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/187_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "91 39 178 161", "used": true}, {"class_name": "cone", "bbox": "3 28 76 128", "used": true}, {"class_name": "cone", "bbox": "223 60 327 210", "used": true}, {"class_name": "cone", "bbox": "259 4 290 53", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/188_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/188_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..ada63c83ec32ed87fe31481c262334ee6a1d95fc
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/188_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "80 35 182 209", "used": true}, {"class_name": "cone", "bbox": "152 14 238 161", "used": true}, {"class_name": "cone", "bbox": "154 84 259 281", "used": true}, {"class_name": "cone", "bbox": "269 72 360 256", "used": true}, {"class_name": "cone", "bbox": "249 26 320 174", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/189_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/189_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..7d63b656cc9882b08788d868041ef7fd60ea986a
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/189_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "27 209 154 481", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/18_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/18_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..8c362fabea3afdb90a519744e30f005845b34c2f
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/18_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "10 100 94 216", "used": true}, {"class_name": "cone", "bbox": "131 104 221 214", "used": true}, {"class_name": "cone", "bbox": "483 140 509 212", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/190_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/190_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..27ece5a442a1940409f18a36b3484cf819a224b1
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/190_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "97 1 166 108", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/191_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/191_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..3b44f0cf9c08beb2494a857ce3f139242c65e53a
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/191_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "52 243 150 460", "used": true}, {"class_name": "cone", "bbox": "172 184 237 315", "used": true}, {"class_name": "cone", "bbox": "230 152 270 235", "used": true}, {"class_name": "cone", "bbox": "257 139 285 203", "used": true}, {"class_name": "cone", "bbox": "279 128 300 180", "used": true}, {"class_name": "cone", "bbox": "290 126 307 165", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/192_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/192_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..3403e45804897f920e33578f7df9ea5f754b8371
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/192_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "171 267 185 295", "used": true}, {"class_name": "cone", "bbox": "337 279 360 322", "used": true}, {"class_name": "cone", "bbox": "308 264 326 292", "used": true}, {"class_name": "cone", "bbox": "127 281 150 323", "used": true}, {"class_name": "cone", "bbox": "105 280 125 321", "used": true}, {"class_name": "cone", "bbox": "461 260 479 288", "used": true}, {"class_name": "cone", "bbox": "433 254 449 280", "used": true}, {"class_name": "cone", "bbox": "412 253 426 275", "used": true}, {"class_name": "cone", "bbox": "395 250 407 271", "used": true}, {"class_name": "cone", "bbox": "371 247 381 265", "used": true}, {"class_name": "cone", "bbox": "494 250 507 270", "used": true}, {"class_name": "cone", "bbox": "456 291 493 339", "used": true}, {"class_name": "cone", "bbox": "318 298 337 339", "used": true}, {"class_name": "cone", "bbox": "170 296 190 339", "used": true}, {"class_name": "cone", "bbox": "194 259 205 281", "used": true}, {"class_name": "cone", "bbox": "363 244 369 262", "used": true}, {"class_name": "cone", "bbox": "295 254 302 273", "used": true}, {"class_name": "cone", "bbox": "151 272 169 306", "used": true}, {"class_name": "cone", "bbox": "20 301 43 339", "used": true}, {"class_name": "cone", "bbox": "62 290 84 339", "used": true}, {"class_name": "cone", "bbox": "365 297 387 339", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/193_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/193_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..7bd82b3ca72ab85f2894724f5dc91291b0ea0178
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/193_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "237 169 296 283", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/194_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/194_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..71076b4d273a736c51a8189f07fa7663043fe643
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/194_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "127 71 261 300", "used": true}, {"class_name": "cone", "bbox": "70 96 168 288", "used": true}, {"class_name": "cone", "bbox": "281 31 449 338", "used": true}, {"class_name": "cone", "bbox": "255 87 350 289", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/195_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/195_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..ca160e6fb942398897691ffa1cb4112d3ebe8288
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/195_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "49 37 353 587", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/196_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/196_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..510e26028b65e8c2fa5ea9040b86b8ff68972159
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/196_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "83 49 258 314", "used": true}, {"class_name": "cone", "bbox": "181 56 233 191", "used": true}, {"class_name": "cone", "bbox": "206 60 250 147", "used": true}, {"class_name": "cone", "bbox": "232 62 255 116", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/197_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/197_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..8f13ecef8811386d95bbaa45dc8b971b27b0f22a
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/197_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "179 43 255 167", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/198_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/198_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..e10ac4050be445221d2575473d7fb23c9d8520b2
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/198_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "51 44 123 240", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/199_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/199_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..dbd8fc2e9498bfde7b09363d80a1b0fe2a64fc09
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/199_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "1 140 179 497", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/19_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/19_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..6df83cf72389a53829f5f3d52a4bb6023130600a
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/19_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "71 40 296 407", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/1_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/1_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..8b2446b24af50d485b4e9e2834917d81f7fb37e3
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/1_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "317 113 373 171", "used": true}, {"class_name": "cone", "bbox": "189 94 255 176", "used": true}, {"class_name": "cone", "bbox": "218 183 421 320", "used": true}, {"class_name": "cone", "bbox": "263 96 289 137", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/200_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/200_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..1ffe88d98e027a1e42f4dbd38f397bc765e42d14
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/200_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "200 59 239 156", "used": true}, {"class_name": "cone", "bbox": "90 175 162 337", "used": true}, {"class_name": "cone", "bbox": "245 21 271 76", "used": true}, {"class_name": "cone", "bbox": "456 112 494 234", "used": true}, {"class_name": "cone", "bbox": "392 50 426 131", "used": true}, {"class_name": "cone", "bbox": "356 12 377 61", "used": true}, {"class_name": "cone", "bbox": "344 1 357 32", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/201_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/201_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..ad170ae76dc77cddefc7bc87587ecca5c5f07742
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/201_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "110 68 178 179", "used": true}, {"class_name": "cone", "bbox": "217 180 340 299", "used": true}, {"class_name": "cone", "bbox": "135 143 224 244", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/202_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/202_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..9733e8d1b697f9929ccbaa7a0c6e84b5cac0cf85
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/202_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "105 33 191 163", "used": true}, {"class_name": "cone", "bbox": "13 25 83 136", "used": true}, {"class_name": "cone", "bbox": "376 90 506 292", "used": true}, {"class_name": "cone", "bbox": "205 168 363 296", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/203_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/203_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..9a5a36d903938c6d14a62b55186679f461e9ce2a
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/203_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "39 101 215 332", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/204_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/204_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..2f37ab463c0362bb7a3a08bae484c39709c0849a
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/204_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "172 54 225 147", "used": true}, {"class_name": "cone", "bbox": "340 53 479 326", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/205_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/205_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..e64a716a3414993b86d317c80272ff798a65a515
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/205_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "233 132 374 368", "used": true}, {"class_name": "cone", "bbox": "154 95 258 270", "used": true}, {"class_name": "cone", "bbox": "89 79 176 219", "used": true}, {"class_name": "cone", "bbox": "33 65 108 183", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/206_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/206_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..3afe611273b75512d3b470139ba506fc3c603e58
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/206_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "103 222 266 432", "used": true}, {"class_name": "cone", "bbox": "55 27 259 191", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/207_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/207_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..60483957ea18fae85c700644023847443f1d3e76
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/207_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "195 93 309 264", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/208_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/208_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..41f60c850aba31f8c2b6ea52fe7c9ed55c29d107
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/208_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "332 52 460 295", "used": true}, {"class_name": "cone", "bbox": "207 131 242 205", "used": true}, {"class_name": "cone", "bbox": "169 147 192 191", "used": true}, {"class_name": "cone", "bbox": "155 155 169 183", "used": true}, {"class_name": "cone", "bbox": "47 167 52 174", "used": false}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/209_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/209_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..f96a5176ce12051d3c886331f6ef88de1b0a2f26
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/209_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "359 94 484 293", "used": true}, {"class_name": "cone", "bbox": "276 104 362 257", "used": true}, {"class_name": "cone", "bbox": "120 168 302 293", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/20_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/20_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..714a6eec7c9affd3e667734ddd8f5bebf50f749c
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/20_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "40 66 229 319", "used": true}, {"class_name": "cone", "bbox": "197 73 312 248", "used": true}, {"class_name": "cone", "bbox": "323 76 387 171", "used": true}, {"class_name": "cone", "bbox": "174 80 243 201", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/210_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/210_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..efd29908dac0e8f159805e7d08ab5e2e6231bd89
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/210_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "207 93 315 266", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/211_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/211_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..c3589f25d8d4a66ec924dc9667e7df64fb791cd0
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/211_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "165 28 243 158", "used": true}, {"class_name": "cone", "bbox": "267 150 359 430", "used": true}, {"class_name": "cone", "bbox": "151 1 189 85", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/212_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/212_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..2d28add495d4614e2a903110050406f74ffb8ba6
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/212_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "248 39 436 328", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/213_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/213_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..620da8763e6a332eb17c7a971dd0d1e45fa11bb9
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/213_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "5 10 137 162", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/214_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/214_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..3d30ebda8d6e8f68b0cb97953f7b39db1f2624fc
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/214_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "135 182 266 462", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/215_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/215_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..0f8821d1b49275f58663c9707c5f4a0b97c43aac
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/215_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "485 480 568 621", "used": true}, {"class_name": "cone", "bbox": "570 412 639 528", "used": true}, {"class_name": "cone", "bbox": "644 361 704 451", "used": true}, {"class_name": "cone", "bbox": "714 308 764 385", "used": true}, {"class_name": "cone", "bbox": "773 273 817 334", "used": true}, {"class_name": "cone", "bbox": "264 615 390 848", "used": true}, {"class_name": "cone", "bbox": "126 707 284 985", "used": true}, {"class_name": "cone", "bbox": "391 642 536 766", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/216_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/216_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..794cf31cae53a46608476bd7bcb9dc40c1b46b5f
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/216_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "103 11 384 322", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/217_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/217_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..c5deafc741a845d1c837a21ca562e9496aa5927e
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/217_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "201 170 294 330", "used": true}, {"class_name": "cone", "bbox": "231 156 306 297", "used": true}, {"class_name": "cone", "bbox": "266 139 342 265", "used": true}, {"class_name": "cone", "bbox": "308 128 370 241", "used": true}, {"class_name": "cone", "bbox": "328 117 388 217", "used": true}, {"class_name": "cone", "bbox": "372 101 421 186", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/218_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/218_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..0832d77f2027ffd92c99fe21e2ac8f97f2b8bef5
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/218_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "47 1 313 265", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/219_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/219_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..5ad1b21e60ab5041ddc6f6ec87541f4b817479d5
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/219_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "298 224 333 273", "used": true}, {"class_name": "cone", "bbox": "246 225 275 274", "used": true}, {"class_name": "cone", "bbox": "186 224 219 273", "used": true}, {"class_name": "cone", "bbox": "134 226 168 269", "used": true}, {"class_name": "cone", "bbox": "348 226 377 270", "used": true}, {"class_name": "cone", "bbox": "379 226 407 269", "used": true}, {"class_name": "cone", "bbox": "99 225 131 267", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/21_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/21_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..71d51f1e4c9b6ac0481400866ade984b832c899f
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/21_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "259 117 388 338", "used": true}, {"class_name": "cone", "bbox": "365 149 509 339", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/220_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/220_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..267dc74a181b3bf4bd469d09c9d5e1f39d1b5003
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/220_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "62 10 291 492", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/221_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/221_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..939cd5ad1b709daa6b7018d35e29ec3b4752c55e
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/221_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "10 16 175 327", "used": true}, {"class_name": "cone", "bbox": "339 18 507 328", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/222_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/222_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..3b587dc1457f67031f81f8a1cce7a992f942d04b
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/222_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "1 82 164 499", "used": true}, {"class_name": "cone", "bbox": "39 1 169 221", "used": true}, {"class_name": "cone", "bbox": "148 1 227 128", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/223_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/223_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..6f3fdc418fcfc98b8b1eed4668f8ef32c0a3e7e4
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/223_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "360 314 649 734", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/224_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/224_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..8a7f87d3f217f88d37e47b2c7f3fa8b3cf6aa4f5
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/224_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "91 172 172 325", "used": true}, {"class_name": "cone", "bbox": "319 174 422 333", "used": true}, {"class_name": "cone", "bbox": "422 156 452 207", "used": true}, {"class_name": "cone", "bbox": "339 152 374 207", "used": true}, {"class_name": "cone", "bbox": "487 156 509 204", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/225_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/225_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..23697f81fd0b966a9dbcdb3a9f51aebf0afdaf0c
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/225_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "50 154 154 320", "used": true}, {"class_name": "cone", "bbox": "147 125 241 279", "used": true}, {"class_name": "cone", "bbox": "225 104 312 250", "used": true}, {"class_name": "cone", "bbox": "286 87 367 226", "used": true}, {"class_name": "cone", "bbox": "339 73 408 202", "used": true}, {"class_name": "cone", "bbox": "379 59 445 185", "used": true}, {"class_name": "cone", "bbox": "414 46 478 163", "used": true}, {"class_name": "cone", "bbox": "459 31 519 145", "used": true}, {"class_name": "cone", "bbox": "497 21 529 127", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/226_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/226_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..97e12b051e4568e2705d188632267a94e611b312
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/226_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "384 235 439 316", "used": true}, {"class_name": "cone", "bbox": "459 234 507 315", "used": true}, {"class_name": "cone", "bbox": "172 213 197 262", "used": true}, {"class_name": "cone", "bbox": "40 194 61 230", "used": true}, {"class_name": "cone", "bbox": "144 209 173 259", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/227_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/227_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..0b39f26ec29371ae7876cb0d62dea4c9db35709a
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/227_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "252 195 299 277", "used": true}, {"class_name": "cone", "bbox": "387 186 433 260", "used": true}, {"class_name": "cone", "bbox": "449 183 492 250", "used": true}, {"class_name": "cone", "bbox": "227 218 247 271", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/228_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/228_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..a7cd1464a1a2254638ca9f83dfc15674381747c1
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/228_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "237 23 438 313", "used": true}, {"class_name": "cone", "bbox": "152 88 233 214", "used": true}, {"class_name": "cone", "bbox": "110 113 166 189", "used": true}, {"class_name": "cone", "bbox": "85 150 106 186", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/229_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/229_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..1dd8506b1b6750990a5286596c477baa04e44737
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/229_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "1 18 148 270", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/22_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/22_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..2c7e97fd135ecabcefd4b799bea7577f22b5b7f8
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/22_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "264 213 314 308", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/230_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/230_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..5931cb8d568b88b0962ce84b1c8f316747b64bab
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/230_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "1 52 106 355", "used": true}, {"class_name": "cone", "bbox": "204 125 234 193", "used": true}, {"class_name": "cone", "bbox": "231 139 257 180", "used": true}, {"class_name": "cone", "bbox": "249 145 261 170", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/231_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/231_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..e4813169182c07598df92615cf47c9f47afbd004
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/231_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "180 139 236 234", "used": true}, {"class_name": "cone", "bbox": "252 126 304 213", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/232_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/232_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..7e093d0eed16cda74efd5179bbf3b393a0997422
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/232_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "280 45 438 274", "used": true}, {"class_name": "cone", "bbox": "79 150 100 186", "used": true}, {"class_name": "cone", "bbox": "167 154 187 184", "used": true}, {"class_name": "cone", "bbox": "203 152 219 187", "used": true}, {"class_name": "cone", "bbox": "255 114 310 221", "used": true}, {"class_name": "cone", "bbox": "245 137 269 201", "used": true}, {"class_name": "cone", "bbox": "231 145 250 195", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/233_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/233_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..14463f8c453d10833be935505dcdfd172232b463
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/233_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "180 306 244 412", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/234_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/234_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..29674c94dce9dea4e03cb2b2e254b7fbaeeff732
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/234_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "100 223 263 439", "used": true}, {"class_name": "cone", "bbox": "51 26 258 188", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/235_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/235_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..58db3a82bd381a13c514ee282d44d3925d946c5d
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/235_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "106 107 640 913", "used": true}, {"class_name": "cone", "bbox": "470 281 936 657", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/236_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/236_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..a4a36b0ab7cd468ee1d67e8972243b9eac94afb5
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/236_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "60 63 198 300", "used": true}, {"class_name": "cone", "bbox": "177 139 380 300", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/237_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/237_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..cfc7116599fb34e69dd3b333e9a65bb7d78ee26e
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/237_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "198 204 279 333", "used": true}, {"class_name": "cone", "bbox": "356 161 432 279", "used": true}, {"class_name": "cone", "bbox": "397 109 455 211", "used": true}, {"class_name": "cone", "bbox": "375 48 420 125", "used": true}, {"class_name": "cone", "bbox": "250 8 290 69", "used": true}, {"class_name": "cone", "bbox": "325 58 375 100", "used": true}, {"class_name": "cone", "bbox": "145 29 197 97", "used": true}, {"class_name": "cone", "bbox": "43 83 103 177", "used": true}, {"class_name": "cone", "bbox": "91 202 165 286", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/238_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/238_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..c193a9772cd0f64eda947400601152bcfcd6fec6
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/238_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "57 168 166 319", "used": true}, {"class_name": "cone", "bbox": "69 125 143 225", "used": true}, {"class_name": "cone", "bbox": "96 73 139 134", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/239_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/239_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..09205831544ac2a124507ca03916b3f74550ba8e
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/239_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "150 218 262 464", "used": true}, {"class_name": "cone", "bbox": "273 243 338 507", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/23_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/23_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..3953d53940465dd644cfb5df0f7e8d3c662fd28c
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/23_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "671 439 773 587", "used": true}, {"class_name": "cone", "bbox": "537 440 653 588", "used": true}, {"class_name": "cone", "bbox": "346 442 457 572", "used": true}, {"class_name": "cone", "bbox": "95 445 214 565", "used": true}, {"class_name": "cone", "bbox": "1 442 59 553", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/240_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/240_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..34824261910cb32e844e16b40d22bc84bbe15289
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/240_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "378 246 411 286", "used": true}, {"class_name": "cone", "bbox": "22 292 79 368", "used": true}, {"class_name": "cone", "bbox": "178 257 205 309", "used": true}, {"class_name": "cone", "bbox": "113 263 145 321", "used": true}, {"class_name": "cone", "bbox": "79 275 117 339", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/241_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/241_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..f0fb351da57e45ac8c09dac1f4cbdc6efb27ab89
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/241_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "10 234 45 272", "used": true}, {"class_name": "cone", "bbox": "63 234 95 271", "used": true}, {"class_name": "cone", "bbox": "114 234 146 269", "used": true}, {"class_name": "cone", "bbox": "165 234 195 268", "used": true}, {"class_name": "cone", "bbox": "216 231 245 267", "used": true}, {"class_name": "cone", "bbox": "264 233 296 268", "used": true}, {"class_name": "cone", "bbox": "316 232 347 268", "used": true}, {"class_name": "cone", "bbox": "367 230 399 268", "used": true}, {"class_name": "cone", "bbox": "419 230 451 265", "used": true}, {"class_name": "cone", "bbox": "469 231 504 265", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/242_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/242_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..7a1d2d4d31e07f561bbcad7481a3bf8f9167c6f7
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/242_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "16 29 261 310", "used": true}, {"class_name": "cone", "bbox": "242 76 439 271", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/243_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/243_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..c6ae16b08b626252bda7851c149c39bcc3193d03
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/243_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "17 254 57 330", "used": true}, {"class_name": "cone", "bbox": "60 254 102 332", "used": true}, {"class_name": "cone", "bbox": "105 252 145 331", "used": true}, {"class_name": "cone", "bbox": "142 252 186 326", "used": true}, {"class_name": "cone", "bbox": "186 249 227 325", "used": true}, {"class_name": "cone", "bbox": "230 248 268 326", "used": true}, {"class_name": "cone", "bbox": "261 251 305 325", "used": true}, {"class_name": "cone", "bbox": "300 250 338 321", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/244_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/244_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..9daa00950c83ab5798c647ff574049a78aaf05fd
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/244_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "166 210 206 273", "used": true}, {"class_name": "cone", "bbox": "52 212 95 275", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/245_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/245_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..8e0600f2ebb074384626532646d274c26e3cd8d8
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/245_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "79 10 306 314", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/246_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/246_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..46b1bfede5c83dc0c0dc9043cd150299b1539f52
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/246_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "129 159 301 476", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/247_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/247_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..024d8325c35071972c81423b2aec08f85e1f561f
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/247_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "5 23 102 86", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/248_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/248_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..2fc14909a20dca6aca622181b5678e2c580e0c74
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/248_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "34 171 193 433", "used": true}, {"class_name": "cone", "bbox": "148 174 339 284", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/249_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/249_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..d71b136fc3bb9c60570ef2d652fb98bb2f7f293b
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/249_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "104 319 141 380", "used": true}, {"class_name": "cone", "bbox": "210 321 246 382", "used": true}, {"class_name": "cone", "bbox": "294 323 329 381", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/24_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/24_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..4b704bce96b637638189af0b8ace07e3c5d37b9f
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/24_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "189 224 316 441", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/250_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/250_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..d598d2ef9b7e848a722dc0c61f57be0b163b5a51
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/250_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "143 114 234 273", "used": true}, {"class_name": "cone", "bbox": "217 152 269 248", "used": true}, {"class_name": "cone", "bbox": "1 1 114 336", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/251_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/251_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..343dd05d83f4133dc7be97fc916d68af89b05867
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/251_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "69 190 223 389", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/252_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/252_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..b7a2f4b106170f9bb8060f37b8fc37047dab7acf
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/252_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "190 275 295 430", "used": true}, {"class_name": "cone", "bbox": "212 260 297 384", "used": false}, {"class_name": "cone", "bbox": "234 234 305 345", "used": true}, {"class_name": "cone", "bbox": "264 226 324 319", "used": false}, {"class_name": "cone", "bbox": "283 214 326 289", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/253_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/253_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..1c15b57bb3708dac77be5e651055acddf1cd06d4
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/253_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "252 1 509 336", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/254_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/254_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..35939f9c1b9c04213ffea96c34062a45ffd80317
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/254_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "305 19 480 298", "used": true}, {"class_name": "cone", "bbox": "150 175 212 262", "used": true}, {"class_name": "cone", "bbox": "125 198 157 257", "used": true}, {"class_name": "cone", "bbox": "97 215 120 254", "used": true}, {"class_name": "cone", "bbox": "86 219 101 254", "used": true}, {"class_name": "cone", "bbox": "52 229 65 249", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/25_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/25_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..45beae5048d549cb7d770dcd619a379b9c8c21d6
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/25_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "204 343 298 477", "used": true}, {"class_name": "cone", "bbox": "71 2 105 47", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/26_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/26_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..2c746108480904f4aceff8cd81ace762c3292291
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/26_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "1 82 165 502", "used": true}, {"class_name": "cone", "bbox": "82 1 165 219", "used": true}, {"class_name": "cone", "bbox": "151 1 225 129", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/27_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/27_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..5ddd7983dc7336c98dd504cdf405888b0837097f
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/27_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "116 52 210 276", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/28_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/28_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..677f79b4598a096b48ab6344f0954ec7d976dd88
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/28_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "208 25 428 311", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/29_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/29_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..1cba20bc210f9dd2911610b76f28c516e1ce4194
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/29_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "14 25 365 432", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/2_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/2_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..f8e6a58f069ec01c106adacf2f639b95e166f51d
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/2_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "73 197 127 303", "used": true}, {"class_name": "cone", "bbox": "101 188 151 276", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/30_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/30_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..8d11a1edbf58b9acd28fa534752585d4eef9a898
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/30_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "58 120 329 307", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/31_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/31_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..12baeb6c70f23635add46cb1b2099f2201dc5d4b
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/31_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "221 110 259 179", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/32_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/32_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..4aaa3d96ac829314ffa03b4110b4e9d74c177b4d
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/32_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "271 42 386 206", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/33_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/33_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..ad864521b5e6362a5efde057b239f8b340fa99dd
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/33_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "145 291 192 369", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/34_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/34_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..224c09be767c1034b210337d73b1696c059ab1af
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/34_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "375 1 479 210", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/35_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/35_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..f910c84d30a987fbcde6f22022f1537a73a9ac9c
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/35_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "292 202 398 293", "used": true}, {"class_name": "cone", "bbox": "130 168 222 270", "used": true}, {"class_name": "cone", "bbox": "52 210 153 335", "used": true}, {"class_name": "cone", "bbox": "183 153 256 218", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/36_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/36_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..48e372d6ffad779151535e325f142695b2143e07
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/36_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "101 88 244 318", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/37_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/37_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..1dec2c10db37c5cd78626d91c713fbe8e87a8c2a
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/37_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "308 168 402 327", "used": true}, {"class_name": "cone", "bbox": "297 120 360 250", "used": true}, {"class_name": "cone", "bbox": "279 92 326 207", "used": true}, {"class_name": "cone", "bbox": "369 24 398 77", "used": true}, {"class_name": "cone", "bbox": "432 20 459 75", "used": true}, {"class_name": "cone", "bbox": "313 21 341 76", "used": true}, {"class_name": "cone", "bbox": "152 16 184 64", "used": true}, {"class_name": "cone", "bbox": "121 15 153 61", "used": true}, {"class_name": "cone", "bbox": "103 15 121 65", "used": true}, {"class_name": "cone", "bbox": "72 16 102 62", "used": true}, {"class_name": "cone", "bbox": "39 17 75 67", "used": true}, {"class_name": "cone", "bbox": "15 17 51 69", "used": true}, {"class_name": "cone", "bbox": "260 74 311 173", "used": true}, {"class_name": "cone", "bbox": "252 61 291 148", "used": true}, {"class_name": "cone", "bbox": "223 30 244 87", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/38_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/38_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..ca7287031fa0e99a5a640c966768b8dcd2a6d1f4
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/38_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "123 257 226 399", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/39_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/39_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..d1b29d5de2027e5fccbeb8757e6b7f53ef4b74b2
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/39_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "87 6 331 409", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/3_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/3_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..b3215d8e0014f3b478946dbb33a9de5923c6a6ef
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/3_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "5 38 336 478", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/40_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/40_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..d9333ffdc11ecc7ef3cd67068a319ef2119babb0
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/40_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "7 150 95 298", "used": true}, {"class_name": "cone", "bbox": "136 152 205 302", "used": true}, {"class_name": "cone", "bbox": "338 155 416 300", "used": true}, {"class_name": "cone", "bbox": "412 158 496 297", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/41_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/41_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..eb534cbfc76d99fc4627e703ce3c957c9de7ef06
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/41_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "158 178 203 247", "used": true}, {"class_name": "cone", "bbox": "165 339 230 416", "used": true}, {"class_name": "cone", "bbox": "158 154 184 196", "used": true}, {"class_name": "cone", "bbox": "186 139 196 153", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/42_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/42_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..b676164d5db47c47aee08bbcbc9c1cc9d5572222
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/42_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "214 38 387 363", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/43_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/43_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..fd6ebb21da5c755e4ad8e1d35dda7fe84574e52e
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/43_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "146 71 228 215", "used": true}, {"class_name": "cone", "bbox": "294 28 407 187", "used": true}, {"class_name": "cone", "bbox": "38 109 114 232", "used": true}, {"class_name": "cone", "bbox": "408 73 497 222", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/44_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/44_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..81f4e82ec824a7b762a40c4f19d434b6d744590e
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/44_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "272 163 338 272", "used": true}, {"class_name": "cone", "bbox": "212 175 271 264", "used": true}, {"class_name": "cone", "bbox": "169 181 223 259", "used": true}, {"class_name": "cone", "bbox": "137 189 183 258", "used": true}, {"class_name": "cone", "bbox": "115 194 145 256", "used": true}, {"class_name": "cone", "bbox": "94 198 124 253", "used": true}, {"class_name": "cone", "bbox": "80 200 104 252", "used": true}, {"class_name": "cone", "bbox": "68 203 89 250", "used": true}, {"class_name": "cone", "bbox": "58 206 73 247", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/45_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/45_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..171bd47653ff0372053377126250b645067aa4c5
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/45_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "80 119 336 509", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/46_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/46_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..0879ade2bf6ed977cbbb830c234cebe94087aaad
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/46_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "75 44 138 133", "used": true}, {"class_name": "cone", "bbox": "279 162 357 282", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/47_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/47_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..31087e468591c023a5825db87a8f1150f0e3ba84
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/47_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "74 115 177 315", "used": true}, {"class_name": "cone", "bbox": "213 130 256 196", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/48_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/48_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..eabb6f0f9d6fd8a674b1cd0bc2ed130fd3f5c036
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/48_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "255 205 314 330", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/49_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/49_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..91a50056361f214d24fe36ba7ff3ded4252f132b
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/49_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "109 61 269 324", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/4_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/4_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..f751933f0d6b1b2e26ab7d4cf1e52caea6f9de21
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/4_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "120 67 222 253", "used": true}, {"class_name": "cone", "bbox": "1 59 102 256", "used": true}, {"class_name": "cone", "bbox": "296 77 400 253", "used": true}, {"class_name": "cone", "bbox": "392 74 479 260", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/50_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/50_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..fbef0b68732e8d0f308a3df3c0f02c2494502d30
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/50_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "45 155 149 319", "used": true}, {"class_name": "cone", "bbox": "152 128 241 277", "used": true}, {"class_name": "cone", "bbox": "228 106 311 247", "used": true}, {"class_name": "cone", "bbox": "295 90 368 227", "used": true}, {"class_name": "cone", "bbox": "339 72 407 200", "used": true}, {"class_name": "cone", "bbox": "381 61 444 184", "used": true}, {"class_name": "cone", "bbox": "420 47 477 165", "used": true}, {"class_name": "cone", "bbox": "464 35 520 145", "used": true}, {"class_name": "cone", "bbox": "496 22 529 123", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/51_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/51_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..3e614c2bf7110d2fbc293a83db4fc327dd7187a8
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/51_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "317 106 435 319", "used": true}, {"class_name": "cone", "bbox": "267 140 340 266", "used": true}, {"class_name": "cone", "bbox": "239 167 272 233", "used": true}, {"class_name": "cone", "bbox": "419 164 470 253", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/52_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/52_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..13a6b0abcdb8834e56987a85d5e9424e301f86ae
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/52_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "23 229 158 394", "used": true}, {"class_name": "cone", "bbox": "171 190 267 307", "used": true}, {"class_name": "cone", "bbox": "257 168 330 260", "used": true}, {"class_name": "cone", "bbox": "311 155 372 229", "used": true}, {"class_name": "cone", "bbox": "355 145 401 208", "used": true}, {"class_name": "cone", "bbox": "388 138 414 191", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/53_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/53_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..c0458e8dd6205631d4d3fde51ff3624d72f38b15
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/53_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "56 3 292 445", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/54_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/54_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..dc2aefc6b17d21063b97b38c09a585a0ac298bc7
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/54_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "207 34 552 668", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/55_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/55_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..3086a7f0f19881a9670217f96a41bf2d832dc41e
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/55_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "683 420 965 934", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/56_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/56_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..6cd1e541dbeb3cb3d80d080c25a70200a379a5f1
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/56_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "20 157 92 246", "used": true}, {"class_name": "cone", "bbox": "188 97 242 161", "used": true}, {"class_name": "cone", "bbox": "314 56 355 108", "used": true}, {"class_name": "cone", "bbox": "406 29 441 73", "used": true}, {"class_name": "cone", "bbox": "465 8 494 45", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/57_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/57_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..e26ab6f71c89ab7578a537c94a1f81e9cb3f4735
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/57_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "1 1 297 756", "used": false}, {"class_name": "cone", "bbox": "48 1 352 523", "used": true}, {"class_name": "cone", "bbox": "183 48 384 418", "used": true}, {"class_name": "cone", "bbox": "299 125 589 360", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/58_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/58_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..b26e4d98bbb103ee8ac772e3b3018f5b4c72d115
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/58_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "112 97 244 345", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/59_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/59_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..2ffbeccf08a9295f0793bd913716f988a03f9f3c
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/59_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "1 219 142 500", "used": true}, {"class_name": "cone", "bbox": "238 240 341 439", "used": true}, {"class_name": "cone", "bbox": "304 283 341 391", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/5_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/5_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..235500fa826e05aa80ac403f54f11947e8be4bc9
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/5_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "218 24 397 294", "used": true}, {"class_name": "cone", "bbox": "130 100 183 188", "used": true}, {"class_name": "cone", "bbox": "111 120 138 167", "used": true}, {"class_name": "cone", "bbox": "101 125 122 156", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/60_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/60_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..1dc9bd2cdf4a2d6374b56860679321854fccc705
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/60_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "123 145 573 970", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/61_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/61_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..8634174c38d8092ed808633d87bd521d96b9119a
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/61_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "105 338 289 547", "used": true}, {"class_name": "cone", "bbox": "229 393 473 594", "used": true}, {"class_name": "cone", "bbox": "538 395 737 650", "used": true}, {"class_name": "cone", "bbox": "413 347 566 498", "used": true}, {"class_name": "cone", "bbox": "650 341 800 466", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/62_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/62_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..de0da706c92321605c921aa58a4b362bf42eebb9
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/62_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "51 125 149 300", "used": true}, {"class_name": "cone", "bbox": "147 108 230 247", "used": true}, {"class_name": "cone", "bbox": "54 12 84 57", "used": true}, {"class_name": "cone", "bbox": "150 10 172 48", "used": true}, {"class_name": "cone", "bbox": "218 8 241 43", "used": true}, {"class_name": "cone", "bbox": "444 43 479 101", "used": true}, {"class_name": "cone", "bbox": "305 156 407 254", "used": true}, {"class_name": "cone", "bbox": "275 10 293 38", "used": true}, {"class_name": "cone", "bbox": "485 35 507 82", "used": true}, {"class_name": "cone", "bbox": "314 9 328 35", "used": true}, {"class_name": "cone", "bbox": "354 9 368 33", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/63_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/63_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..c385e12a22b42e47f324386c316804e4f0db3abd
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/63_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "171 26 254 178", "used": true}, {"class_name": "cone", "bbox": "2 179 189 474", "used": true}, {"class_name": "cone", "bbox": "252 1 301 85", "used": true}, {"class_name": "cone", "bbox": "286 1 322 43", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/64_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/64_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..9efd271305db04624e3250dbe64dde6e4cae4db8
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/64_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "190 89 270 217", "used": true}, {"class_name": "cone", "bbox": "236 36 289 123", "used": true}, {"class_name": "cone", "bbox": "73 244 146 359", "used": true}, {"class_name": "cone", "bbox": "249 11 291 77", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/65_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/65_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..d458f338bbe8a81bcf968aba2f022b22d3699a97
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/65_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "69 257 329 648", "used": true}, {"class_name": "cone", "bbox": "649 239 922 621", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/66_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/66_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..838b8c142f50fc64e2cbdc1b7cd18071709b0467
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/66_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "120 115 555 963", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/67_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/67_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..676f635b26779dc05b6e02e3247cffb792c9df11
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/67_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "25 80 225 418", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/68_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/68_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..7d5b428ea789b74043e95977c64de50a71a3ba2f
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/68_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "172 201 457 594", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/69_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/69_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..a3514bc6304a8146b68e78e5a9c2869305527635
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/69_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "113 207 280 461", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/6_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/6_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..f0e5dd33741ef63adf91fa308aabb6f95bf6c03f
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/6_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "61 52 124 139", "used": true}, {"class_name": "cone", "bbox": "413 45 474 131", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/70_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/70_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..924a7e0deb3bcbb87c4fb1d074dbdc702c82ce7a
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/70_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "39 317 122 427", "used": true}, {"class_name": "cone", "bbox": "30 288 72 361", "used": true}, {"class_name": "cone", "bbox": "17 278 46 332", "used": true}, {"class_name": "cone", "bbox": "7 266 26 298", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/71_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/71_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..87a9e598271624dd21ff66aa01604efd3e2ad058
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/71_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "80 49 295 298", "used": true}, {"class_name": "cone", "bbox": "216 81 399 271", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/72_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/72_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..03145d771c30da111959165de840fa0e1a53450a
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/72_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "351 214 417 300", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/73_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/73_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..170cd60ae85d5316302390e0080c9b53f59297be
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/73_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "6 155 173 456", "used": true}, {"class_name": "cone", "bbox": "71 23 224 371", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/74_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/74_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..f4bf45005883e4dbdf65135cb4b8a34c8e0411c3
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/74_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "192 189 333 325", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/75_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/75_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..881730176e61e644ffc904e4a8d7bd7a44405c59
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/75_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "130 151 282 426", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/76_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/76_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..e5194e6e0c4ba17666a218d39141a4c52212da2d
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/76_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "16 23 80 135", "used": true}, {"class_name": "cone", "bbox": "113 34 190 164", "used": true}, {"class_name": "cone", "bbox": "375 90 506 291", "used": true}, {"class_name": "cone", "bbox": "205 165 363 301", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/77_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/77_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..dfef808c074322578fb765f5594aacbcc4ccdaa2
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/77_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "1 17 309 504", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/78_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/78_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..9af412829f99f0d124f0d2a69cf8665e9769b061
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/78_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "14 9 320 477", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/79_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/79_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..f60d8a766da0c2202ee61cba6f009423439e5570
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/79_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "186 49 357 287", "used": true}, {"class_name": "cone", "bbox": "381 166 449 263", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/7_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/7_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..4e42290ce44cc4025b8ebdf18fb6f80045425f72
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/7_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "232 226 308 334", "used": true}, {"class_name": "cone", "bbox": "481 213 511 306", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/80_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/80_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..eebbd6ccb8e1afe51c87ddf59cf2764e5e212468
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/80_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "142 133 285 303", "used": true}, {"class_name": "cone", "bbox": "323 84 443 213", "used": true}, {"class_name": "cone", "bbox": "40 111 171 245", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/81_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/81_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..60c7dc5260d6360cb11cdcda16aa06371308b5c5
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/81_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "16 18 280 499", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/82_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/82_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..b13f488dd0071a77812b3fda5d15deb4142920a0
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/82_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "130 155 304 477", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/83_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/83_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..0cea701b0ecd6d2404955c359b5f3b836f068c5f
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/83_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "73 48 258 314", "used": true}, {"class_name": "cone", "bbox": "168 59 232 189", "used": true}, {"class_name": "cone", "bbox": "201 61 251 147", "used": true}, {"class_name": "cone", "bbox": "224 63 255 116", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/84_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/84_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..737ab2c0cae23ec1034de6d3ae40bcfc3dd13632
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/84_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "153 404 194 486", "used": true}, {"class_name": "cone", "bbox": "115 399 152 479", "used": true}, {"class_name": "cone", "bbox": "187 402 230 480", "used": true}, {"class_name": "cone", "bbox": "213 401 251 470", "used": true}, {"class_name": "cone", "bbox": "95 401 122 464", "used": true}, {"class_name": "cone", "bbox": "185 385 205 445", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/85_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/85_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..86cd66e33b5e092258a9b5d0feff57298e651927
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/85_ground_truth.json	
@@ -0,0 +1 @@
+[{"class_name": "cone", "bbox": "97 87 234 335", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/86_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/86_ground_truth.json
new file mode 100644
index 0000000000000000000000000000000000000000..940bf184bf8e39416e3a1b7af97127e450c9a9b5
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/86_ground_truth.json	
@@ -0,0 +1 @@
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/87_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/87_ground_truth.json
new file mode 100644
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/87_ground_truth.json	
@@ -0,0 +1 @@
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/88_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/88_ground_truth.json
new file mode 100644
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+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/88_ground_truth.json	
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/89_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/89_ground_truth.json
new file mode 100644
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+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/89_ground_truth.json	
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/8_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/8_ground_truth.json
new file mode 100644
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/8_ground_truth.json	
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/90_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/90_ground_truth.json
new file mode 100644
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+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/90_ground_truth.json	
@@ -0,0 +1 @@
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/91_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/91_ground_truth.json
new file mode 100644
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/91_ground_truth.json	
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/92_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/92_ground_truth.json
new file mode 100644
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+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/92_ground_truth.json	
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/93_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/93_ground_truth.json
new file mode 100644
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+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/93_ground_truth.json	
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/94_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/94_ground_truth.json
new file mode 100644
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+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/94_ground_truth.json	
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/95_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/95_ground_truth.json
new file mode 100644
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+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/95_ground_truth.json	
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/96_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/96_ground_truth.json
new file mode 100644
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/96_ground_truth.json	
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/97_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/97_ground_truth.json
new file mode 100644
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+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/97_ground_truth.json	
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/98_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/98_ground_truth.json
new file mode 100644
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+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/98_ground_truth.json	
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/99_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/99_ground_truth.json
new file mode 100644
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+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/99_ground_truth.json	
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/9_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/9_ground_truth.json
new file mode 100644
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+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/9_ground_truth.json	
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/cone_predictions.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/cone_predictions.json
new file mode 100644
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/results.txt b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/results.txt
new file mode 100644
index 0000000000000000000000000000000000000000..f9ecc226a1300ccf38d9f01a65304880809a09e2
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/results.txt	
@@ -0,0 +1,8 @@
+# AP and precision/recall per class
+99.334% = cone AP  
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'1.000', '1.000', '1.000', '1.000', '1.000', '1.000', '1.000', '1.000', '1.000', '1.000', '1.000', '1.000', '1.000', '1.000', '1.000', '1.000', '1.000', '1.000', '1.000', '1.000', '1.000', '1.000', '1.000', '1.000', '1.000', '1.000', '1.000', '1.000', '1.000', '1.000', '1.000', '1.000', '1.000', '1.000', '1.000', '1.000', '1.000', '1.000', '1.000', '1.000', '1.000', '1.000', '1.000', '1.000', '1.000', '1.000', '1.000', '1.000', '1.000', '1.000', '1.000', '1.000', '1.000', '1.000', '1.000', '1.000', '1.000', '1.000', '1.000', '1.000', '1.000', '1.000', '1.000', '1.000', '1.000', '1.000', '1.000', '1.000', '1.000', '1.000', '1.000', '1.000', '1.000', '1.000', '1.000', '1.000', '1.000', '1.000', '1.000', '1.000', '1.000', '1.000', '1.000', '1.000', '1.000', '0.999', '0.999', '0.999', '0.999', '0.999', '0.999', '0.999', '0.999', '0.999', '0.999', '0.999', '0.997', '0.997', '0.997', '0.996', '0.996', '0.995', '0.995', '0.993', '0.993', '0.993', '0.992', '0.992', '0.992', '0.991', '0.991', '0.991', '0.990', '0.988', '0.987', '0.987', '0.986', '0.986', '0.985', '0.983', '0.982', '0.982', '0.981', '0.981', '0.980', '0.980', '0.978', '0.977', '0.976', '0.975', '0.974', '0.972', '0.971', '0.970', '0.969', '0.969', '0.968', '0.966', '0.000']
+ Recall   :['0.000', '0.001', '0.003', '0.004', '0.005', '0.006', '0.008', '0.009', '0.010', '0.012', '0.013', '0.014', '0.015', '0.017', '0.018', '0.019', '0.021', '0.022', '0.023', '0.024', '0.026', '0.027', '0.028', '0.030', '0.031', '0.032', '0.033', '0.035', '0.036', '0.037', '0.039', '0.040', '0.041', '0.042', '0.044', '0.045', '0.046', '0.047', '0.049', '0.050', '0.051', '0.053', '0.054', '0.055', '0.056', '0.058', '0.059', '0.060', '0.062', '0.063', '0.064', '0.065', '0.067', '0.068', '0.069', '0.071', '0.072', '0.073', '0.074', '0.076', '0.077', '0.078', '0.080', '0.081', '0.082', '0.083', '0.085', '0.086', '0.087', '0.089', '0.090', '0.091', '0.092', '0.094', '0.095', '0.096', '0.098', '0.099', '0.100', '0.101', '0.103', '0.104', '0.105', '0.107', '0.108', '0.109', '0.110', '0.112', '0.113', '0.114', '0.116', '0.117', '0.118', '0.119', '0.121', '0.122', '0.123', '0.125', '0.126', '0.127', '0.128', '0.130', '0.131', '0.132', '0.134', '0.135', '0.136', '0.137', '0.139', '0.140', '0.141', '0.142', '0.144', '0.145', '0.146', '0.148', '0.149', '0.150', '0.151', '0.153', '0.154', '0.155', '0.157', '0.158', '0.159', '0.160', '0.162', '0.163', '0.164', '0.166', '0.167', '0.168', '0.169', '0.171', '0.172', '0.173', '0.175', '0.176', '0.177', '0.178', '0.180', '0.181', '0.182', '0.184', '0.185', '0.186', '0.187', '0.189', '0.190', '0.191', '0.193', '0.194', '0.195', '0.196', '0.198', '0.199', '0.200', '0.202', '0.203', '0.204', '0.205', '0.207', '0.208', '0.209', '0.211', '0.212', '0.213', '0.214', '0.216', '0.217', '0.218', '0.220', '0.221', '0.222', '0.223', '0.225', '0.226', '0.227', '0.228', '0.230', '0.231', '0.232', '0.234', '0.235', '0.236', '0.237', '0.239', '0.240', '0.241', '0.243', '0.244', '0.245', '0.246', '0.248', '0.249', '0.250', '0.252', '0.253', '0.254', '0.255', '0.257', '0.258', '0.259', '0.261', '0.262', '0.263', '0.264', '0.266', '0.267', '0.268', '0.270', '0.271', '0.272', '0.273', '0.275', '0.276', '0.277', '0.279', '0.280', '0.281', '0.282', '0.284', '0.285', '0.286', '0.288', '0.289', '0.290', '0.291', '0.293', '0.294', '0.295', '0.297', '0.298', '0.299', '0.300', '0.302', '0.303', '0.304', '0.306', '0.307', '0.308', '0.309', '0.311', '0.312', '0.313', '0.315', '0.316', '0.317', '0.318', '0.320', '0.321', '0.322', '0.323', '0.325', '0.326', '0.327', '0.329', '0.330', '0.331', '0.332', '0.334', '0.335', '0.336', '0.338', '0.339', '0.340', '0.341', '0.343', '0.344', '0.345', '0.347', '0.348', '0.349', '0.350', '0.352', '0.353', '0.354', '0.356', '0.357', '0.358', '0.359', '0.361', '0.362', '0.363', '0.365', '0.366', '0.367', '0.368', '0.370', '0.371', '0.372', '0.374', '0.375', '0.376', '0.377', '0.379', '0.380', '0.381', '0.383', '0.384', '0.385', '0.386', '0.388', '0.389', '0.390', '0.392', '0.393', '0.394', '0.395', '0.397', '0.398', '0.399', '0.401', '0.402', '0.403', '0.404', '0.406', '0.407', '0.408', '0.409', '0.411', '0.412', '0.413', '0.415', '0.416', '0.417', '0.418', '0.420', '0.421', '0.422', '0.424', '0.425', '0.426', '0.427', '0.429', '0.430', '0.431', '0.433', '0.434', '0.435', '0.436', '0.438', '0.439', '0.440', '0.442', '0.443', '0.444', '0.445', '0.447', '0.448', '0.449', '0.451', '0.452', '0.453', '0.454', '0.456', '0.457', '0.458', '0.460', '0.461', '0.462', '0.463', '0.465', '0.466', '0.467', '0.469', '0.470', '0.471', '0.472', '0.474', '0.475', '0.476', '0.478', '0.479', '0.480', '0.481', '0.483', '0.484', '0.485', '0.487', '0.488', '0.489', '0.490', '0.492', '0.493', '0.494', '0.496', '0.497', '0.498', '0.499', '0.501', '0.502', '0.503', '0.504', '0.506', '0.507', '0.508', '0.510', '0.511', '0.512', '0.513', '0.515', '0.516', '0.517', '0.519', '0.520', '0.521', '0.522', '0.524', '0.525', '0.526', '0.528', '0.529', '0.530', '0.531', '0.533', '0.534', '0.535', '0.537', '0.538', '0.539', '0.540', '0.542', '0.543', '0.544', '0.546', '0.547', '0.548', '0.549', '0.551', '0.552', '0.553', '0.555', '0.556', '0.557', '0.558', '0.560', '0.561', '0.562', '0.564', '0.565', '0.566', '0.567', '0.569', '0.570', '0.571', '0.573', '0.574', '0.575', '0.576', '0.578', '0.579', '0.580', '0.582', '0.583', '0.584', '0.585', '0.587', '0.588', '0.589', '0.591', '0.592', '0.593', '0.594', '0.596', '0.597', '0.598', '0.599', '0.601', '0.602', '0.603', '0.605', '0.606', '0.607', '0.608', '0.610', '0.611', '0.612', '0.614', '0.615', '0.616', '0.617', '0.619', '0.620', '0.621', '0.623', '0.624', '0.625', '0.626', '0.628', '0.629', '0.630', '0.632', '0.633', '0.634', '0.635', '0.637', '0.638', '0.639', '0.641', '0.642', '0.643', '0.644', '0.646', '0.647', '0.648', '0.650', '0.651', '0.652', '0.653', '0.655', '0.656', '0.657', '0.659', '0.660', '0.661', '0.662', '0.664', '0.665', '0.666', '0.668', '0.669', '0.670', '0.671', '0.673', '0.674', '0.675', '0.677', '0.678', '0.679', '0.680', '0.682', '0.683', '0.684', '0.685', '0.687', '0.688', '0.689', '0.691', '0.692', '0.693', '0.694', '0.696', '0.697', '0.698', '0.700', '0.701', '0.702', '0.703', '0.705', '0.706', '0.707', '0.709', '0.710', '0.711', '0.712', '0.714', '0.715', '0.716', '0.718', '0.719', '0.720', '0.721', '0.723', '0.724', '0.725', '0.727', '0.728', '0.729', '0.730', '0.732', '0.733', '0.734', '0.736', '0.737', '0.738', '0.739', '0.741', '0.742', '0.743', '0.745', '0.746', '0.747', '0.748', '0.750', '0.751', '0.752', '0.754', '0.755', '0.756', '0.757', '0.759', '0.760', '0.761', '0.763', '0.764', '0.765', '0.766', '0.768', '0.769', '0.770', '0.772', '0.773', '0.774', '0.775', '0.777', '0.778', '0.779', '0.780', '0.782', '0.783', '0.784', '0.786', '0.787', '0.788', '0.789', '0.791', '0.792', '0.793', '0.795', '0.796', '0.797', '0.798', '0.800', '0.801', '0.802', '0.804', '0.805', '0.806', '0.807', '0.809', '0.810', '0.811', '0.813', '0.814', '0.815', '0.816', '0.818', '0.819', '0.820', '0.822', '0.823', '0.824', '0.825', '0.827', '0.828', '0.829', '0.831', '0.832', '0.833', '0.834', '0.836', '0.837', '0.838', '0.840', '0.841', '0.842', '0.843', '0.845', '0.846', '0.847', '0.849', '0.850', '0.851', '0.852', '0.854', '0.855', '0.856', '0.858', '0.859', '0.860', '0.861', '0.863', '0.864', '0.865', '0.866', '0.868', '0.869', '0.870', '0.872', '0.873', '0.874', '0.875', '0.877', '0.878', '0.879', '0.881', '0.882', '0.883', '0.884', '0.886', '0.887', '0.888', '0.890', '0.891', '0.892', '0.893', '0.895', '0.896', '0.897', '0.899', '0.900', '0.901', '0.902', '0.904', '0.905', '0.906', '0.908', '0.909', '0.910', '0.911', '0.913', '0.914', '0.915', '0.917', '0.918', '0.919', '0.920', '0.922', '0.923', '0.924', '0.926', '0.927', '0.928', '0.929', '0.931', '0.932', '0.933', '0.935', '0.936', '0.937', '0.938', '0.940', '0.941', '0.942', '0.944', '0.945', '0.946', '0.947', '0.949', '0.950', '0.951', '0.953', '0.954', '0.955', '0.956', '0.958', '0.959', '0.960', '0.960', '0.961', '0.963', '0.964', '0.965', '0.967', '0.968', '0.969', '0.970', '0.972', '0.973', '0.973', '0.974', '0.976', '0.976', '0.977', '0.977', '0.978', '0.978', '0.979', '0.981', '0.981', '0.982', '0.983', '0.983', '0.985', '0.986', '0.986', '0.986', '0.986', '0.987', '0.987', '0.988', '0.988', '0.988', '0.988', '0.990', '0.990', '0.991', '0.991', '0.992', '0.992', '0.992', '0.992', '0.992', '0.992', '0.992', '0.992', '0.992', '0.992', '0.994', '0.994', '0.994', '1.000']
+
+
+# mAP of all classes
+mAP = 99.334%, 12.29 FPS
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mnist/make_data.py b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mnist/make_data.py
new file mode 100644
index 0000000000000000000000000000000000000000..3f5044d5f86b632bedb676144737b450a274ad87
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mnist/make_data.py	
@@ -0,0 +1,138 @@
+#================================================================
+#
+#   File name   : make_data.py
+#   Author      : PyLessons
+#   Created date: 2020-04-20
+#   Website     : https://pylessons.com/
+#   GitHub      : https://github.com/pythonlessons/TensorFlow-2.x-YOLOv3
+#   Description : create mnist example dataset to train custom yolov3
+#
+#================================================================
+import os
+os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
+import cv2
+import numpy as np
+import shutil
+import random
+from zipfile import ZipFile 
+
+SIZE = 416
+images_num_train = 1000
+images_num_test = 200
+
+image_sizes = [3, 6, 3] # small, medium, big
+
+# this helps to run script both from terminal and python IDLE
+add_path = "mnist"
+if os.getcwd().split(os.sep)[-1] != "mnist":
+    add_path = "mnist"
+    os.chdir(add_path)
+else:
+    add_path = ""  
+    
+def compute_iou(box1, box2):
+    # xmin, ymin, xmax, ymax
+    A1 = (box1[2] - box1[0])*(box1[3] - box1[1])
+    A2 = (box2[2] - box2[0])*(box2[3] - box2[1])
+
+    xmin = max(box1[0], box2[0])
+    ymin = max(box1[1], box2[1])
+    xmax = min(box1[2], box2[2])
+    ymax = min(box1[3], box2[3])
+
+    if ymin >= ymax or xmin >= xmax: return 0
+    return  ((xmax-xmin) * (ymax - ymin)) / (A1 + A2)
+
+
+def make_image(data, image_path, ratio=1):
+    blank = data[0]
+    boxes = data[1]
+    label = data[2]
+
+    ID = image_path.split("/")[-1][0]
+    image = cv2.imread(image_path)
+    image = cv2.resize(image, (int(28*ratio), int(28*ratio)))
+    h, w, c = image.shape
+
+    while True:
+        xmin = np.random.randint(0, SIZE-w, 1)[0]
+        ymin = np.random.randint(0, SIZE-h, 1)[0]
+        xmax = xmin + w
+        ymax = ymin + h
+        box = [xmin, ymin, xmax, ymax]
+
+        iou = [compute_iou(box, b) for b in boxes]
+        if max(iou) < 0.02:
+            boxes.append(box)
+            label.append(ID)
+            break
+
+    for i in range(w):
+        for j in range(h):
+            x = xmin + i
+            y = ymin + j
+            blank[y][x] = image[j][i]
+
+    # cv2.rectangle(blank, (xmin, ymin), (xmax, ymax), [0, 0, 255], 2)
+    return blank
+
+
+for file in ["train", "test"]:
+    if not os.path.exists(f"mnist/{file}"):
+        with ZipFile(f"mnist/{file}.zip", 'r') as zip:
+            # extracting all the files 
+            print(f'Extracting all {file} files now...') 
+            zip.extractall()
+            shutil.move(file, "mnist")
+            print('Done!')
+
+for file in ['train','test']:
+    images_path = os.getcwd()+f"/mnist_{file}"
+    labels_txt = os.getcwd()+f"/mnist_{file}.txt"
+    
+    if file == 'train': images_num = images_num_train
+    if file == 'test': images_num = images_num_test
+        
+    if os.path.exists(images_path): shutil.rmtree(images_path)
+    os.mkdir(images_path)
+
+    image_paths  = [os.path.join(os.path.realpath("."), os.getcwd()+f"/mnist/{file}/" + image_name)
+                           for image_name in os.listdir(os.getcwd()+f"/mnist/{file}")]
+    
+    with open(labels_txt, "w") as wf:
+        image_num = 0
+        while image_num < images_num:
+            image_path = os.path.realpath(os.path.join(images_path, "%06d.jpg" %(image_num+1)))
+            #print(image_path)
+            annotation = image_path
+            blanks = np.ones(shape=[SIZE, SIZE, 3]) * 255
+            bboxes = [[0,0,1,1]]
+            labels = [0]
+            data = [blanks, bboxes, labels]
+            bboxes_num = 0
+            
+            # ratios small, medium, big objects
+            ratios = [[0.5, 0.8], [1., 1.5, 2.], [3., 4.]]
+            for i in range(len(ratios)):
+                N = random.randint(0, image_sizes[i])
+                if N !=0: bboxes_num += 1
+                for _ in range(N):
+                    ratio = random.choice(ratios[i])
+                    idx = random.randint(0, len(image_paths)-1)
+                    data[0] = make_image(data, image_paths[idx], ratio)
+
+            if bboxes_num == 0: continue
+            cv2.imwrite(image_path, data[0])
+            for i in range(len(labels)):
+                if i == 0: continue
+                xmin = str(bboxes[i][0])
+                ymin = str(bboxes[i][1])
+                xmax = str(bboxes[i][2])
+                ymax = str(bboxes[i][3])
+                class_ind = str(labels[i])
+                annotation += ' ' + ','.join([xmin, ymin, xmax, ymax, str(class_ind)])
+            image_num += 1
+            print("=> %s" %annotation)
+            wf.write(annotation + "\n")
+
+if add_path != "": os.chdir("..")
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mnist/mnist.names b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mnist/mnist.names
new file mode 100644
index 0000000000000000000000000000000000000000..8b1acc12b635c26f3decadeaa251729d3ce512e9
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mnist/mnist.names	
@@ -0,0 +1,10 @@
+0
+1
+2
+3
+4
+5
+6
+7
+8
+9
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mnist/mnist/test.zip b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mnist/mnist/test.zip
new file mode 100644
index 0000000000000000000000000000000000000000..94850dc2313a83f2d7c756181070c7e7a8573c15
Binary files /dev/null and b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mnist/mnist/test.zip differ
diff --git a/PAR 152/Yolo Tensorflow/model_data/coco/train2017.txt b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mnist/mnist/train.zip
similarity index 67%
rename from PAR 152/Yolo Tensorflow/model_data/coco/train2017.txt
rename to PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mnist/mnist/train.zip
index b3756b01731277d4b7131bbeb17ea18511c84578..ce8b46a83514818d80bba47380e79f92a7d05c99 100644
Binary files a/PAR 152/Yolo Tensorflow/model_data/coco/train2017.txt and b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mnist/mnist/train.zip differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mnist/show_image.py b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mnist/show_image.py
new file mode 100644
index 0000000000000000000000000000000000000000..7327d470d8aafa0f1a8a225ef0a48bbf981a0c03
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mnist/show_image.py	
@@ -0,0 +1,30 @@
+#================================================================
+#
+#   File name   : show_image.py
+#   Author      : PyLessons
+#   Created date: 2020-04-20
+#   Website     : https://pylessons.com/
+#   GitHub      : https://github.com/pythonlessons/TensorFlow-2.x-YOLOv3
+#   Description : show random image from created dataset
+#
+#================================================================
+import random
+import cv2
+import numpy as np
+from PIL import Image
+
+ID = random.randint(0, 200)
+label_txt = "./mnist_train.txt"
+image_info = open(label_txt).readlines()[ID].split()
+
+image_path = image_info[0]
+image = cv2.imread(image_path)
+for bbox in image_info[1:]:
+    bbox = bbox.split(",")
+    image = cv2.rectangle(image,(int(float(bbox[0])),
+                                 int(float(bbox[1]))),
+                                (int(float(bbox[2])),
+                                 int(float(bbox[3]))), (255,0,0), 2)
+
+image = Image.fromarray(np.uint8(image))
+image.show()
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/object_tracker.py b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/object_tracker.py
new file mode 100644
index 0000000000000000000000000000000000000000..c23388ad1e1ecebc34e26bb3564080d9eac0b422
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/object_tracker.py	
@@ -0,0 +1,151 @@
+#================================================================
+#
+#   File name   : object_tracker.py
+#   Author      : PyLessons
+#   Created date: 2020-09-17
+#   Website     : https://pylessons.com/
+#   GitHub      : https://github.com/pythonlessons/TensorFlow-2.x-YOLOv3
+#   Description : code to track detected object from video or webcam
+#
+#================================================================
+import os
+os.environ['CUDA_VISIBLE_DEVICES'] = '0'
+import cv2
+import numpy as np
+import tensorflow as tf
+from yolov3.utils import Load_Yolo_model, image_preprocess, postprocess_boxes, nms, draw_bbox, read_class_names
+from yolov3.configs import *
+import time
+
+from deep_sort import nn_matching
+from deep_sort.detection import Detection
+from deep_sort.tracker import Tracker
+from deep_sort import generate_detections as gdet
+
+video_path   = "./IMAGES/test.mp4"
+
+def Object_tracking(Yolo, video_path, output_path, input_size=416, show=False, CLASSES=YOLO_COCO_CLASSES, score_threshold=0.3, iou_threshold=0.45, rectangle_colors='', Track_only = []):
+    # Definition of the parameters
+    max_cosine_distance = 0.7
+    nn_budget = None
+    
+    #initialize deep sort object
+    model_filename = 'model_data/mars-small128.pb'
+    encoder = gdet.create_box_encoder(model_filename, batch_size=1)
+    metric = nn_matching.NearestNeighborDistanceMetric("cosine", max_cosine_distance, nn_budget)
+    tracker = Tracker(metric)
+
+    times, times_2 = [], []
+
+    if video_path:
+        vid = cv2.VideoCapture(video_path) # detect on video
+    else:
+        vid = cv2.VideoCapture(0) # detect from webcam
+
+    # by default VideoCapture returns float instead of int
+    width = int(vid.get(cv2.CAP_PROP_FRAME_WIDTH))
+    height = int(vid.get(cv2.CAP_PROP_FRAME_HEIGHT))
+    fps = int(vid.get(cv2.CAP_PROP_FPS))
+    codec = cv2.VideoWriter_fourcc(*'XVID')
+    out = cv2.VideoWriter(output_path, codec, fps, (width, height)) # output_path must be .mp4
+
+    NUM_CLASS = read_class_names(CLASSES)
+    key_list = list(NUM_CLASS.keys()) 
+    val_list = list(NUM_CLASS.values())
+    while True:
+        _, frame = vid.read()
+
+        try:
+            original_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+            original_frame = cv2.cvtColor(original_frame, cv2.COLOR_BGR2RGB)
+        except:
+            break
+        
+        image_data = image_preprocess(np.copy(original_frame), [input_size, input_size])
+        #image_data = tf.expand_dims(image_data, 0)
+        image_data = image_data[np.newaxis, ...].astype(np.float32)
+
+        t1 = time.time()
+        if YOLO_FRAMEWORK == "tf":
+            pred_bbox = Yolo.predict(image_data)
+        elif YOLO_FRAMEWORK == "trt":
+            batched_input = tf.constant(image_data)
+            result = Yolo(batched_input)
+            pred_bbox = []
+            for key, value in result.items():
+                value = value.numpy()
+                pred_bbox.append(value)
+        
+        #t1 = time.time()
+        #pred_bbox = Yolo.predict(image_data)
+        t2 = time.time()
+        
+        pred_bbox = [tf.reshape(x, (-1, tf.shape(x)[-1])) for x in pred_bbox]
+        pred_bbox = tf.concat(pred_bbox, axis=0)
+
+        bboxes = postprocess_boxes(pred_bbox, original_frame, input_size, score_threshold)
+        bboxes = nms(bboxes, iou_threshold, method='nms')
+
+        # extract bboxes to boxes (x, y, width, height), scores and names
+        boxes, scores, names = [], [], []
+        for bbox in bboxes:
+            if len(Track_only) !=0 and NUM_CLASS[int(bbox[5])] in Track_only or len(Track_only) == 0:
+                boxes.append([bbox[0].astype(int), bbox[1].astype(int), bbox[2].astype(int)-bbox[0].astype(int), bbox[3].astype(int)-bbox[1].astype(int)])
+                scores.append(bbox[4])
+                names.append(NUM_CLASS[int(bbox[5])])
+
+        # Obtain all the detections for the given frame.
+        boxes = np.array(boxes) 
+        names = np.array(names)
+        scores = np.array(scores)
+        features = np.array(encoder(original_frame, boxes))
+        detections = [Detection(bbox, score, class_name, feature) for bbox, score, class_name, feature in zip(boxes, scores, names, features)]
+
+        # Pass detections to the deepsort object and obtain the track information.
+        tracker.predict()
+        tracker.update(detections)
+
+        # Obtain info from the tracks
+        tracked_bboxes = []
+        for track in tracker.tracks:
+            if not track.is_confirmed() or track.time_since_update > 5:
+                continue 
+            bbox = track.to_tlbr() # Get the corrected/predicted bounding box
+            class_name = track.get_class() #Get the class name of particular object
+            tracking_id = track.track_id # Get the ID for the particular track
+            index = key_list[val_list.index(class_name)] # Get predicted object index by object name
+            tracked_bboxes.append(bbox.tolist() + [tracking_id, index]) # Structure data, that we could use it with our draw_bbox function
+
+        # draw detection on frame
+        image = draw_bbox(original_frame, tracked_bboxes, CLASSES=CLASSES, tracking=True)
+
+        t3 = time.time()
+        times.append(t2-t1)
+        times_2.append(t3-t1)
+        
+        times = times[-20:]
+        times_2 = times_2[-20:]
+
+        ms = sum(times)/len(times)*1000
+        fps = 1000 / ms
+        fps2 = 1000 / (sum(times_2)/len(times_2)*1000)
+        
+        image = cv2.putText(image, "Time: {:.1f} FPS".format(fps), (0, 30), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (0, 0, 255), 2)
+
+        # draw original yolo detection
+        #image = draw_bbox(image, bboxes, CLASSES=CLASSES, show_label=False, rectangle_colors=rectangle_colors, tracking=True)
+
+        print("Time: {:.2f}ms, Detection FPS: {:.1f}, total FPS: {:.1f}".format(ms, fps, fps2))
+        if output_path != '': out.write(image)
+        if show:
+            cv2.imshow('output', image)
+            
+            if cv2.waitKey(25) & 0xFF == ord("q"):
+                cv2.destroyAllWindows()
+                break
+            
+    cv2.destroyAllWindows()
+
+
+yolo = Load_Yolo_model()
+Object_tracking(yolo, video_path, "detection.mp4", input_size=YOLO_INPUT_SIZE, show=True, iou_threshold=0.1, rectangle_colors=(255,0,0), Track_only = ["person"])
diff --git a/PAR 152/Yolo Tensorflow/requirements.txt b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/requirements.txt
similarity index 58%
rename from PAR 152/Yolo Tensorflow/requirements.txt
rename to PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/requirements.txt
index 3fbb849f24cab0a1f6e3d002ba71c83626d6effd..96aa428d2e4cca5331b1f4ce23fdf4014174ae7e 100644
--- a/PAR 152/Yolo Tensorflow/requirements.txt	
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/requirements.txt	
@@ -2,9 +2,8 @@ numpy>=1.18.2
 scipy>=1.4.1
 wget>=3.2
 seaborn>=0.10.0
-tensorflow==2.3.1
-tensorflow-gpu==2.3.1
-opencv-python==4.1.2.30
+tensorflow
+opencv-python==4.4.0.46
 tqdm==4.43.0
 pandas
 awscli
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/tools/Convert_to_TRT.py b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/tools/Convert_to_TRT.py
new file mode 100644
index 0000000000000000000000000000000000000000..46eb988be1c498592d7fb81f0dedde7b746400e9
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/tools/Convert_to_TRT.py	
@@ -0,0 +1,48 @@
+#================================================================
+#
+#   File name   : Convert_to_TRT.py
+#   Author      : PyLessons
+#   Created date: 2020-08-17
+#   Website     : https://pylessons.com/
+#   GitHub      : https://github.com/pythonlessons/TensorFlow-2.x-YOLOv3
+#   Description : convert TF frozen graph to TensorRT model
+#
+#================================================================
+import os
+os.environ['CUDA_VISIBLE_DEVICES'] = '0'
+import sys
+
+foldername = os.path.basename(os.getcwd())
+if foldername == "tools":
+    os.chdir("..")
+sys.path.insert(1, os.getcwd())
+    
+import tensorflow as tf
+import numpy as np
+physical_devices = tf.config.experimental.list_physical_devices('GPU')
+if len(physical_devices) > 0:
+    tf.config.experimental.set_memory_growth(physical_devices[0], True)
+from yolov3.configs import *
+from tensorflow.python.compiler.tensorrt import trt_convert as trt
+
+def calibration_input():
+    for i in range(100):
+        batched_input = np.random.random((1, YOLO_INPUT_SIZE, YOLO_INPUT_SIZE, 3)).astype(np.float32)
+        batched_input = tf.constant(batched_input)
+        yield (batched_input,)
+
+conversion_params = trt.DEFAULT_TRT_CONVERSION_PARAMS
+conversion_params = conversion_params._replace(max_workspace_size_bytes=4000000000)
+conversion_params = conversion_params._replace(precision_mode=YOLO_TRT_QUANTIZE_MODE)
+conversion_params = conversion_params._replace(max_batch_size=1)
+if YOLO_TRT_QUANTIZE_MODE == 'INT8':
+    conversion_params = conversion_params._replace(use_calibration=True)
+
+converter = trt.TrtGraphConverterV2(input_saved_model_dir=f'./checkpoints/{YOLO_TYPE}-{YOLO_INPUT_SIZE}', conversion_params=conversion_params)
+if YOLO_TRT_QUANTIZE_MODE == 'INT8':
+    converter.convert(calibration_input_fn=calibration_input)
+else:
+    converter.convert()
+
+converter.save(output_saved_model_dir=f'./checkpoints/{YOLO_TYPE}-trt-{YOLO_TRT_QUANTIZE_MODE}-{YOLO_INPUT_SIZE}')
+print(f'Done Converting to TensorRT, model saved to: /checkpoints/{YOLO_TYPE}-trt-{YOLO_TRT_QUANTIZE_MODE}-{YOLO_INPUT_SIZE}')
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/tools/Convert_to_pb.py b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/tools/Convert_to_pb.py
new file mode 100644
index 0000000000000000000000000000000000000000..1ce98bcf4ac211dff43dd41a5b52cfabad9e408c
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/tools/Convert_to_pb.py	
@@ -0,0 +1,40 @@
+#================================================================
+#
+#   File name   : Convert_to_pb.py
+#   Author      : PyLessons
+#   Created date: 2020-08-17
+#   Website     : https://pylessons.com/
+#   GitHub      : https://github.com/pythonlessons/TensorFlow-2.x-YOLOv3
+#   Description : used to freeze tf model to .pb model
+#
+#================================================================
+import os
+os.environ['CUDA_VISIBLE_DEVICES'] = '0'
+import sys
+
+foldername = os.path.basename(os.getcwd())
+if foldername == "tools":
+    os.chdir("..")
+sys.path.insert(1, os.getcwd())
+
+import tensorflow as tf
+from yolov3.yolov4 import Create_Yolo
+from yolov3.utils import load_yolo_weights
+from yolov3.configs import *
+
+if YOLO_TYPE == "yolov4":
+    Darknet_weights = YOLO_V4_TINY_WEIGHTS if TRAIN_YOLO_TINY else YOLO_V4_WEIGHTS
+if YOLO_TYPE == "yolov3":
+    Darknet_weights = YOLO_V3_TINY_WEIGHTS if TRAIN_YOLO_TINY else YOLO_V3_WEIGHTS
+
+if YOLO_CUSTOM_WEIGHTS == False:
+    yolo = Create_Yolo(input_size=YOLO_INPUT_SIZE, CLASSES=YOLO_COCO_CLASSES)
+    load_yolo_weights(yolo, Darknet_weights) # use Darknet weights
+else:
+    yolo = Create_Yolo(input_size=YOLO_INPUT_SIZE, CLASSES=TRAIN_CLASSES)
+    yolo.load_weights(YOLO_CUSTOM_WEIGHTS) # use custom weights
+
+yolo.summary()
+yolo.save(f'./checkpoints/{YOLO_TYPE}-{YOLO_INPUT_SIZE}')
+
+print(f"model saves to /checkpoints/{YOLO_TYPE}-{YOLO_INPUT_SIZE}")
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/tools/Detection_to_XML.py b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/tools/Detection_to_XML.py
new file mode 100644
index 0000000000000000000000000000000000000000..5cfcf6dfa29a9f90ae46aaebfcee98a3d2f9d362
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/tools/Detection_to_XML.py	
@@ -0,0 +1,125 @@
+#================================================================
+#
+#   File name   : Detection_to_XML.py
+#   Author      : PyLessons
+#   Created date: 2020-09-27
+#   Website     : https://pylessons.com/
+#   GitHub      : https://github.com/pythonlessons/TensorFlow-2.x-YOLOv3
+#   Description : converts YOLO detection to XML file
+#
+#===============================================================
+from textwrap import dedent
+from lxml import etree
+import glob
+import os
+import cv2
+import time
+
+def CreateXMLfile(path, file_name, image, bboxes, NUM_CLASS):
+    boxes = []
+    for bbox in bboxes:
+        boxes.append([bbox[0].astype(int), bbox[1].astype(int), bbox[2].astype(int), bbox[3].astype(int), NUM_CLASS[int(bbox[5])]])#, bbox[4], NUM_CLASS[int(bbox[5])]])
+
+    if not os.path.exists(path):
+        os.makedirs(path)
+    os.chdir(path)
+
+    img_name = "XML_"+file_name+".png"
+    
+    cv2.imwrite(img_name,image)
+
+    annotation = etree.Element("annotation")
+
+    folder = etree.Element("folder")
+    folder.text = os.path.basename(os.getcwd())
+    annotation.append(folder)
+
+    filename_xml = etree.Element("filename")
+    filename_str = img_name.split(".")[0]
+    filename_xml.text = img_name
+    annotation.append(filename_xml)
+
+    path = etree.Element("path")
+    path.text = os.path.join(os.getcwd(), filename_str + ".jpg")
+    annotation.append(path)
+
+    source = etree.Element("source")
+    annotation.append(source)
+
+    database = etree.Element("database")
+    database.text = "Unknown"
+    source.append(database)
+
+    size = etree.Element("size")
+    annotation.append(size)
+
+    width = etree.Element("width")
+    height = etree.Element("height")
+    depth = etree.Element("depth")
+
+    img = cv2.imread(filename_xml.text)
+
+    width.text = str(img.shape[1])
+    height.text = str(img.shape[0])
+    depth.text = str(img.shape[2])
+
+    size.append(width)
+    size.append(height)
+    size.append(depth)
+
+    segmented = etree.Element("segmented")
+    segmented.text = "0"
+    annotation.append(segmented)
+
+    for Object in boxes:
+        class_name = Object[4]
+        xmin_l = str(int(float(Object[0])))
+        ymin_l = str(int(float(Object[1])))
+        xmax_l = str(int(float(Object[2])))
+        ymax_l = str(int(float(Object[3])))
+
+        obj = etree.Element("object")
+        annotation.append(obj)
+
+        name = etree.Element("name")
+        name.text = class_name
+        obj.append(name)
+
+        pose = etree.Element("pose")
+        pose.text = "Unspecified"
+        obj.append(pose)
+
+        truncated = etree.Element("truncated")
+        truncated.text = "0"
+        obj.append(truncated)
+
+        difficult = etree.Element("difficult")
+        difficult.text = "0"
+        obj.append(difficult)
+
+        bndbox = etree.Element("bndbox")
+        obj.append(bndbox)
+
+        xmin = etree.Element("xmin")
+        xmin.text = xmin_l
+        bndbox.append(xmin)
+
+        ymin = etree.Element("ymin")
+        ymin.text = ymin_l
+        bndbox.append(ymin)
+
+        xmax = etree.Element("xmax")
+        xmax.text = xmax_l
+        bndbox.append(xmax)
+
+        ymax = etree.Element("ymax")
+        ymax.text = ymax_l
+        bndbox.append(ymax)
+
+    # write xml to file
+    s = etree.tostring(annotation, pretty_print=True)
+    with open(filename_str + ".xml", 'wb') as f:
+        f.write(s)
+        f.close()
+
+    os.chdir("..")
diff --git a/PAR 152/Yolo Tensorflow/tools/XML_to_YOLOv3.py b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/tools/XML_to_YOLOv3.py
similarity index 77%
rename from PAR 152/Yolo Tensorflow/tools/XML_to_YOLOv3.py
rename to PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/tools/XML_to_YOLOv3.py
index d5665d350f469c8454f06e340efd954697e31585..7f9d78aacb4d4669c1a4e188a1a54b0f4fa21112 100644
--- a/PAR 152/Yolo Tensorflow/tools/XML_to_YOLOv3.py	
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/tools/XML_to_YOLOv3.py	
@@ -1,3 +1,13 @@
+#================================================================
+#
+#   File name   : XML_to_YOLOv3.py
+#   Author      : PyLessons
+#   Created date: 2020-06-04
+#   Website     : https://pylessons.com/
+#   GitHub      : https://github.com/pythonlessons/TensorFlow-2.x-YOLOv3
+#   Description : used to convert XML labels to YOLOv3 training labels
+#
+#================================================================
 import xml.etree.ElementTree as ET
 import os
 import glob
@@ -7,9 +17,9 @@ if foldername == "tools": os.chdir("..")
 
 
 data_dir = '/custom_dataset/'
-Dataset_names_path = "model_data/custom_data_names.txt"
-Dataset_train = "model_data/custom_data_train.txt"
-Dataset_test = "model_data/custom_data_test.txt"
+Dataset_names_path = "model_data/cone.txt"
+Dataset_train = "model_data/cone_train.txt"
+Dataset_test = "model_data/cone_test.txt"
 is_subfolder = False
 
 Dataset_names = []
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/tools/oid_to_pascal_voc_xml.py b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/tools/oid_to_pascal_voc_xml.py
new file mode 100644
index 0000000000000000000000000000000000000000..3accbfcd237a6ca415c3635421d1722765cb04f2
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/tools/oid_to_pascal_voc_xml.py	
@@ -0,0 +1,175 @@
+#================================================================
+#
+#   File name   : oid_to_pascal_vos_xml.py
+#   Author      : PyLessons
+#   Created date: 2020-06-04
+#   Website     : https://pylessons.com/
+#   GitHub      : https://github.com/pythonlessons/TensorFlow-2.x-YOLOv3
+#   Description : used to convert oid labels to pascal vos xml
+#
+#================================================================
+import os
+from tqdm import tqdm
+from sys import exit
+import argparse
+import cv2
+from textwrap import dedent
+from lxml import etree
+
+foldername = os.path.basename(os.getcwd())
+if foldername == "tools": os.chdir("..")
+
+Dataset_path = "OIDv4_ToolKit/OID/Dataset"
+
+def convert_to_xml():
+    current_path = os.getcwd()
+    os.chdir(Dataset_path)
+    DIRS = os.listdir(os.getcwd())
+
+    for DIR in DIRS:
+        if os.path.isdir(DIR):
+            os.chdir(DIR)
+
+            print("Currently in Subdirectory:", DIR)
+            CLASS_DIRS = os.listdir(os.getcwd()) 
+            for CLASS_DIR in CLASS_DIRS:
+                if " " in CLASS_DIR:
+                    os.rename(CLASS_DIR, CLASS_DIR.replace(" ", "_"))
+            
+            CLASS_DIRS = os.listdir(os.getcwd())
+            for CLASS_DIR in CLASS_DIRS:
+                if os.path.isdir(CLASS_DIR):
+                    os.chdir(CLASS_DIR)
+
+                    print("\n" + "Creating PASCAL VOC XML Files for Class:", CLASS_DIR)
+                    # Create Directory for annotations if it does not exist yet
+
+                    #Read Labels from OIDv4 ToolKit
+                    os.chdir("Label")
+
+                    #Create PASCAL XML
+                    for filename in tqdm(os.listdir(os.getcwd())):
+                        if filename.endswith(".txt"):
+                            filename_str = str.split(filename, ".")[0]
+
+
+                            annotation = etree.Element("annotation")
+                            
+                            os.chdir("..")
+                            folder = etree.Element("folder")
+                            folder.text = os.path.basename(os.getcwd())
+                            annotation.append(folder)
+
+                            filename_xml = etree.Element("filename")
+                            filename_xml.text = filename_str + ".jpg"
+                            annotation.append(filename_xml)
+
+                            path = etree.Element("path")
+                            path.text = os.path.join(os.path.dirname(os.path.abspath(filename)), filename_str + ".jpg")
+                            annotation.append(path)
+
+                            source = etree.Element("source")
+                            annotation.append(source)
+
+                            database = etree.Element("database")
+                            database.text = "Unknown"
+                            source.append(database)
+
+                            size = etree.Element("size")
+                            annotation.append(size)
+
+                            width = etree.Element("width")
+                            height = etree.Element("height")
+                            depth = etree.Element("depth")
+
+                            img = cv2.imread(filename_xml.text)
+
+                            try:
+                                width.text = str(img.shape[1])
+                            except AttributeError:
+                                os.chdir("Label")
+                                continue
+                            height.text = str(img.shape[0])
+                            depth.text = str(img.shape[2])
+
+                            size.append(width)
+                            size.append(height)
+                            size.append(depth)
+
+                            segmented = etree.Element("segmented")
+                            segmented.text = "0"
+                            annotation.append(segmented)
+
+                            os.chdir("Label")
+                            label_original = open(filename, 'r')
+
+                            # Labels from OIDv4 Toolkit: name_of_class X_min Y_min X_max Y_max
+                            for line in label_original:
+                                line = line.strip()
+                                l = line.split(' ')
+                                
+                                class_name_len = len(l) - 4 # 4 coordinates
+                                class_name = l[0]
+                                for i in range(1,class_name_len):
+                                    class_name = f"{class_name}_{l[i]}"
+
+                                addi = class_name_len
+
+                                xmin_l = str(int(round(float(l[0+addi]))))
+                                ymin_l = str(int(round(float(l[1+addi]))))
+                                xmax_l = str(int(round(float(l[2+addi]))))
+                                ymax_l = str(int(round(float(l[3+addi]))))
+                                
+                                obj = etree.Element("object")
+                                annotation.append(obj)
+
+                                name = etree.Element("name")
+                                name.text = class_name
+                                obj.append(name)
+
+                                pose = etree.Element("pose")
+                                pose.text = "Unspecified"
+                                obj.append(pose)
+
+                                truncated = etree.Element("truncated")
+                                truncated.text = "0"
+                                obj.append(truncated)
+
+                                difficult = etree.Element("difficult")
+                                difficult.text = "0"
+                                obj.append(difficult)
+
+                                bndbox = etree.Element("bndbox")
+                                obj.append(bndbox)
+
+                                xmin = etree.Element("xmin")
+                                xmin.text = xmin_l
+                                bndbox.append(xmin)
+
+                                ymin = etree.Element("ymin")
+                                ymin.text = ymin_l
+                                bndbox.append(ymin)
+
+                                xmax = etree.Element("xmax")
+                                xmax.text = xmax_l
+                                bndbox.append(xmax)
+
+                                ymax = etree.Element("ymax")
+                                ymax.text = ymax_l
+                                bndbox.append(ymax)
+
+                            os.chdir("..")
+                            # write xml to file
+                            s = etree.tostring(annotation, pretty_print=True)
+                            with open(filename_str + ".xml", 'wb') as f:
+                                f.write(s)
+                                f.close()
+
+                            os.chdir("Label")
+
+                    os.chdir("..")
+                    os.chdir("..")   
+                       
+            os.chdir("..")
+
+convert_to_xml()
diff --git a/PAR 152/Yolo Tensorflow/train.py b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/train.py
similarity index 87%
rename from PAR 152/Yolo Tensorflow/train.py
rename to PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/train.py
index 24f0f042243e7d688785f4d6c2a2d32070ff15bf..be0198509ad92f62b36e82121e650d35d0d055af 100644
--- a/PAR 152/Yolo Tensorflow/train.py	
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/train.py	
@@ -1,5 +1,16 @@
+#================================================================
+#
+#   File name   : train.py
+#   Author      : PyLessons
+#   Created date: 2020-08-06
+#   Website     : https://pylessons.com/
+#   GitHub      : https://github.com/pythonlessons/TensorFlow-2.x-YOLOv3
+#   Description : used to train custom object detector
+#
+#================================================================
 import os
 os.environ['CUDA_VISIBLE_DEVICES'] = '0'
+os.environ['TF_FORCE_GPU_ALLOW_GROWTH'] = 'true'
 from tensorflow.python.client import device_lib
 print(device_lib.list_local_devices())
 import shutil
@@ -7,10 +18,13 @@ import numpy as np
 import tensorflow as tf
 #from tensorflow.keras.utils import plot_model
 from yolov3.dataset import Dataset
+from yolov3.yolov4 import Create_Yolo, compute_loss
 from yolov3.utils import load_yolo_weights
 from yolov3.configs import *
 from evaluate_mAP import get_mAP
     
+if YOLO_TYPE == "yolov4":
+    Darknet_weights = YOLO_V4_TINY_WEIGHTS if TRAIN_YOLO_TINY else YOLO_V4_WEIGHTS
 if YOLO_TYPE == "yolov3":
     Darknet_weights = YOLO_V3_TINY_WEIGHTS if TRAIN_YOLO_TINY else YOLO_V3_WEIGHTS
 if TRAIN_YOLO_TINY: TRAIN_MODEL_NAME += "_Tiny"
@@ -42,7 +56,7 @@ def main():
     yolo = Create_Yolo(input_size=YOLO_INPUT_SIZE, training=True, CLASSES=TRAIN_CLASSES)
     if TRAIN_FROM_CHECKPOINT:
         try:
-            yolo.load_weights(TRAIN_FROM_CHECKPOINT)
+            yolo.load_weights(f"./checkpoints/{TRAIN_MODEL_NAME}")
         except ValueError:
             print("Shapes are incompatible, transfering Darknet weights")
             TRAIN_FROM_CHECKPOINT = False
@@ -164,8 +178,11 @@ def main():
             yolo.save_weights(save_directory)
 
     # measure mAP of trained custom model
-    mAP_model.load_weights(save_directory) # use keras weights
-    get_mAP(mAP_model, testset, score_threshold=TEST_SCORE_THRESHOLD, iou_threshold=TEST_IOU_THRESHOLD)
-
+    try:
+        mAP_model.load_weights(save_directory) # use keras weights
+        get_mAP(mAP_model, testset, score_threshold=TEST_SCORE_THRESHOLD, iou_threshold=TEST_IOU_THRESHOLD)
+    except UnboundLocalError:
+        print("You don't have saved model weights to measure mAP, check TRAIN_SAVE_BEST_ONLY and TRAIN_SAVE_CHECKPOINT lines in configs.py")
+        
 if __name__ == '__main__':
     main()
diff --git a/PAR 152/Yolo Tensorflow/yolov3/__ init __.py b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/yolov3/__ init __.py
similarity index 100%
rename from PAR 152/Yolo Tensorflow/yolov3/__ init __.py
rename to PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/yolov3/__ init __.py
diff --git a/PAR 152/Yolo Tensorflow/yolov3/dataset.py b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/yolov3/dataset.py
similarity index 76%
rename from PAR 152/Yolo Tensorflow/yolov3/dataset.py
rename to PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/yolov3/dataset.py
index 5e8c5c3f9c8186594341c44ce07f90bf19158ae9..3666bc61ed587a8c7469bd795955943a99dad0ef 100644
--- a/PAR 152/Yolo Tensorflow/yolov3/dataset.py	
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/yolov3/dataset.py	
@@ -1,3 +1,14 @@
+#================================================================
+#
+#   File name   : dataset.py
+#   Author      : PyLessons
+#   Created date: 2020-07-31
+#   Website     : https://pylessons.com/
+#   GitHub      : https://github.com/pythonlessons/TensorFlow-2.x-YOLOv3
+#   Description : functions used to prepare dataset for custom training
+#
+#================================================================
+# TODO: transfer numpy to tensorflow operations
 import os
 import cv2
 import random
@@ -16,6 +27,7 @@ class Dataset(object):
         self.batch_size  = TRAIN_BATCH_SIZE if dataset_type == 'train' else TEST_BATCH_SIZE
         self.data_aug    = TRAIN_DATA_AUG   if dataset_type == 'train' else TEST_DATA_AUG
 
+        self.train_yolo_tiny = TRAIN_YOLO_TINY
         self.train_input_sizes = TRAIN_INPUT_SIZE
         self.strides = np.array(YOLO_STRIDES)
         self.classes = read_class_names(TRAIN_CLASSES)
@@ -33,10 +45,23 @@ class Dataset(object):
     def load_annotations(self, dataset_type):
         final_annotations = []
         with open(self.annot_path, 'r') as f:
-            txt = f.readlines()
+            txt = f.read().splitlines()
             annotations = [line.strip() for line in txt if len(line.strip().split()[1:]) != 0]
         np.random.shuffle(annotations)
-        
+
+        # for annotation in annotations:
+        #     image_extension = '.jpg'
+        #     extension_index = annotation.find(image_extension)
+        #     image_path = annotation[:extension_index+len(image_extension)]
+        #     line = annotation[extension_index+len(image_extension):].split()
+        #     if not os.path.exists(image_path):
+        #         raise KeyError("%s does not exist ... " %image_path)
+        #     if TRAIN_LOAD_IMAGES_TO_RAM:
+        #         image = cv2.imread(image_path)
+        #     else:
+        #         image = ''
+        #     final_annotations.append([image_path, line, image])
+        # return final_annotations
         for annotation in annotations:
             # fully parse annotations
             line = annotation.split()
@@ -74,7 +99,7 @@ class Dataset(object):
                 if bad_image_name not in i:
                     f.write(i)
             f.truncate()
-    
+
     def __next__(self):
         with tf.device('/cpu:0'):
             self.train_input_size = random.choice([self.train_input_sizes])
@@ -82,14 +107,16 @@ class Dataset(object):
 
             batch_image = np.zeros((self.batch_size, self.train_input_size, self.train_input_size, 3), dtype=np.float32)
 
-            batch_label_sbbox = np.zeros((self.batch_size, self.train_output_sizes[0], self.train_output_sizes[0],
-                                          self.anchor_per_scale, 5 + self.num_classes), dtype=np.float32)
-            batch_label_mbbox = np.zeros((self.batch_size, self.train_output_sizes[1], self.train_output_sizes[1],
-                                          self.anchor_per_scale, 5 + self.num_classes), dtype=np.float32)
-            batch_label_lbbox = np.zeros((self.batch_size, self.train_output_sizes[2], self.train_output_sizes[2],
-                                          self.anchor_per_scale, 5 + self.num_classes), dtype=np.float32)
+            if self.train_yolo_tiny:
+                batch_label_mbbox = np.zeros((self.batch_size, self.train_output_sizes[0], self.train_output_sizes[0], self.anchor_per_scale, 5 + self.num_classes), dtype=np.float32)
+                batch_label_lbbox = np.zeros((self.batch_size, self.train_output_sizes[1], self.train_output_sizes[1], self.anchor_per_scale, 5 + self.num_classes), dtype=np.float32)
+            else:
+                batch_label_sbbox = np.zeros((self.batch_size, self.train_output_sizes[0], self.train_output_sizes[0], self.anchor_per_scale, 5 + self.num_classes), dtype=np.float32)
+                batch_label_mbbox = np.zeros((self.batch_size, self.train_output_sizes[1], self.train_output_sizes[1], self.anchor_per_scale, 5 + self.num_classes), dtype=np.float32)
+                batch_label_lbbox = np.zeros((self.batch_size, self.train_output_sizes[2], self.train_output_sizes[2], self.anchor_per_scale, 5 + self.num_classes), dtype=np.float32)
+
+                batch_sbboxes = np.zeros((self.batch_size, self.max_bbox_per_scale, 4), dtype=np.float32)
 
-            batch_sbboxes = np.zeros((self.batch_size, self.max_bbox_per_scale, 4), dtype=np.float32)
             batch_mbboxes = np.zeros((self.batch_size, self.max_bbox_per_scale, 4), dtype=np.float32)
             batch_lbboxes = np.zeros((self.batch_size, self.max_bbox_per_scale, 4), dtype=np.float32)
 
@@ -102,29 +129,37 @@ class Dataset(object):
                     annotation = self.annotations[index]
                     image, bboxes = self.parse_annotation(annotation)
                     try:
-                        label_sbbox, label_mbbox, label_lbbox, sbboxes, mbboxes, lbboxes = self.preprocess_true_boxes(bboxes)
+                        if self.train_yolo_tiny:
+                            label_mbbox, label_lbbox, mbboxes, lbboxes = self.preprocess_true_boxes(bboxes)
+                        else:
+                            label_sbbox, label_mbbox, label_lbbox, sbboxes, mbboxes, lbboxes = self.preprocess_true_boxes(bboxes)
                     except IndexError:
                         exceptions = True
                         self.Delete_bad_annotation(annotation)
                         print("IndexError, something wrong with", annotation[0], "removed this line from annotation file")
 
                     batch_image[num, :, :, :] = image
-                    batch_label_sbbox[num, :, :, :, :] = label_sbbox
                     batch_label_mbbox[num, :, :, :, :] = label_mbbox
                     batch_label_lbbox[num, :, :, :, :] = label_lbbox
-                    batch_sbboxes[num, :, :] = sbboxes
                     batch_mbboxes[num, :, :] = mbboxes
                     batch_lbboxes[num, :, :] = lbboxes
+                    if not self.train_yolo_tiny:
+                        batch_label_sbbox[num, :, :, :, :] = label_sbbox
+                        batch_sbboxes[num, :, :] = sbboxes
+
                     num += 1
 
-                if exceptions: 
+                if exceptions:
                     print('\n')
                     raise Exception("There were problems with dataset, I fixed them, now restart the training process.")
                 self.batch_count += 1
-                batch_smaller_target = batch_label_sbbox, batch_sbboxes
+                if not self.train_yolo_tiny:
+                    batch_smaller_target = batch_label_sbbox, batch_sbboxes
                 batch_medium_target  = batch_label_mbbox, batch_mbboxes
                 batch_larger_target  = batch_label_lbbox, batch_lbboxes
 
+                if self.train_yolo_tiny:
+                    return batch_image, (batch_medium_target, batch_larger_target)
                 return batch_image, (batch_smaller_target, batch_medium_target, batch_larger_target)
             else:
                 self.batch_count = 0
@@ -189,7 +224,7 @@ class Dataset(object):
         else:
             image_path = annotation[0]
             image = cv2.imread(image_path)
-            
+
         bboxes = np.array([list(map(int, box.split(','))) for box in annotation[1]])
 
         if self.data_aug:
@@ -198,17 +233,19 @@ class Dataset(object):
             image, bboxes = self.random_translate(np.copy(image), np.copy(bboxes))
 
         #image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
-        if mAP == True: 
+        if mAP == True:
             return image, bboxes
-        
+
         image, bboxes = image_preprocess(np.copy(image), [self.input_sizes, self.input_sizes], np.copy(bboxes))
         return image, bboxes
 
     def preprocess_true_boxes(self, bboxes):
+        OUTPUT_LEVELS = len(self.strides)
+
         label = [np.zeros((self.train_output_sizes[i], self.train_output_sizes[i], self.anchor_per_scale,
-                           5 + self.num_classes)) for i in range(3)]
-        bboxes_xywh = [np.zeros((self.max_bbox_per_scale, 4)) for _ in range(3)]
-        bbox_count = np.zeros((3,))
+                           5 + self.num_classes)) for i in range(OUTPUT_LEVELS)]
+        bboxes_xywh = [np.zeros((self.max_bbox_per_scale, 4)) for _ in range(OUTPUT_LEVELS)]
+        bbox_count = np.zeros((OUTPUT_LEVELS,))
 
         for bbox in bboxes:
             bbox_coor = bbox[:4]
@@ -225,7 +262,7 @@ class Dataset(object):
 
             iou = []
             exist_positive = False
-            for i in range(3):
+            for i in range(OUTPUT_LEVELS):#range(3):
                 anchors_xywh = np.zeros((self.anchor_per_scale, 4))
                 anchors_xywh[:, 0:2] = np.floor(bbox_xywh_scaled[i, 0:2]).astype(np.int32) + 0.5
                 anchors_xywh[:, 2:4] = self.anchors[i]
@@ -262,13 +299,15 @@ class Dataset(object):
                 bbox_ind = int(bbox_count[best_detect] % self.max_bbox_per_scale)
                 bboxes_xywh[best_detect][bbox_ind, :4] = bbox_xywh
                 bbox_count[best_detect] += 1
+
+        if self.train_yolo_tiny:
+            label_mbbox, label_lbbox = label
+            mbboxes, lbboxes = bboxes_xywh
+            return label_mbbox, label_lbbox, mbboxes, lbboxes
+
         label_sbbox, label_mbbox, label_lbbox = label
         sbboxes, mbboxes, lbboxes = bboxes_xywh
         return label_sbbox, label_mbbox, label_lbbox, sbboxes, mbboxes, lbboxes
 
     def __len__(self):
         return self.num_batchs
-
-
-
-
diff --git a/PAR 152/Yolo Tensorflow/yolov3/utils.py b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/yolov3/utils.py
similarity index 90%
rename from PAR 152/Yolo Tensorflow/yolov3/utils.py
rename to PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/yolov3/utils.py
index 1e30f811221a7ff76ffdeda727c43bc78e86e71f..0cc8f6b66d497353d2aee47bb0f31de2a76b745e 100644
--- a/PAR 152/Yolo Tensorflow/yolov3/utils.py	
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/yolov3/utils.py	
@@ -1,3 +1,13 @@
+#================================================================
+#
+#   File name   : utils.py
+#   Author      : PyLessons
+#   Created date: 2020-09-27
+#   Website     : https://pylessons.com/
+#   GitHub      : https://github.com/pythonlessons/TensorFlow-2.x-YOLOv3
+#   Description : additional yolov3 and yolov4 functions
+#
+#================================================================
 from multiprocessing import Process, Queue, Pipe
 import cv2
 import time
@@ -6,16 +16,18 @@ import colorsys
 import numpy as np
 import tensorflow as tf
 from yolov3.configs import *
-from yolov3.yolov3 import *
+from yolov3.yolov4 import *
 from tensorflow.python.saved_model import tag_constants
 
-
 def load_yolo_weights(model, weights_file):
     tf.keras.backend.clear_session() # used to reset layer names
     # load Darknet original weights to TensorFlow model
     if YOLO_TYPE == "yolov3":
         range1 = 75 if not TRAIN_YOLO_TINY else 13
         range2 = [58, 66, 74] if not TRAIN_YOLO_TINY else [9, 12]
+    if YOLO_TYPE == "yolov4":
+        range1 = 110 if not TRAIN_YOLO_TINY else 21
+        range2 = [93, 101, 109] if not TRAIN_YOLO_TINY else [17, 20]
     
     with open(weights_file, 'rb') as wf:
         major, minor, revision, seen, _ = np.fromfile(wf, dtype=np.int32, count=5)
@@ -69,15 +81,22 @@ def Load_Yolo_model():
         except RuntimeError: pass
         
     if YOLO_FRAMEWORK == "tf": # TensorFlow detection
+        if YOLO_TYPE == "yolov4":
+            Darknet_weights = YOLO_V4_TINY_WEIGHTS if TRAIN_YOLO_TINY else YOLO_V4_WEIGHTS
         if YOLO_TYPE == "yolov3":
             Darknet_weights = YOLO_V3_TINY_WEIGHTS if TRAIN_YOLO_TINY else YOLO_V3_WEIGHTS
             
         if YOLO_CUSTOM_WEIGHTS == False:
+            print("Loading Darknet_weights from:", Darknet_weights)
             yolo = Create_Yolo(input_size=YOLO_INPUT_SIZE, CLASSES=YOLO_COCO_CLASSES)
             load_yolo_weights(yolo, Darknet_weights) # use Darknet weights
         else:
+            checkpoint = f"./checkpoints/{TRAIN_MODEL_NAME}"
+            if TRAIN_YOLO_TINY:
+                checkpoint += "_Tiny"
+            print("Loading custom weights from:", checkpoint)
             yolo = Create_Yolo(input_size=YOLO_INPUT_SIZE, CLASSES=TRAIN_CLASSES)
-            yolo.load_weights(YOLO_CUSTOM_WEIGHTS) # use custom weights
+            yolo.load_weights(checkpoint)  # use custom weights
         
     elif YOLO_FRAMEWORK == "trt": # TensorRT detection
         saved_model_loaded = tf.saved_model.load(YOLO_CUSTOM_WEIGHTS, tags=[tag_constants.SERVING])
@@ -86,8 +105,6 @@ def Load_Yolo_model():
 
     return yolo
 
-
-
 def image_preprocess(image, target_size, gt_boxes=None):
     ih, iw    = target_size
     h,  w, _  = image.shape
@@ -142,7 +159,11 @@ def draw_bbox(image, bboxes, CLASSES=YOLO_COCO_CLASSES, show_label=True, show_co
 
             if tracking: score_str = " "+str(score)
 
-            label = "{}".format(NUM_CLASS[class_ind]) + score_str
+            try:
+                label = "{}".format(NUM_CLASS[class_ind]) + score_str
+            except KeyError:
+                print("You received KeyError, this might be that you are trying to use yolo original weights")
+                print("while using custom classes, if using custom model in configs.py set YOLO_CUSTOM_WEIGHTS = True")
 
             # get text size
             (text_width, text_height), baseline = cv2.getTextSize(label, cv2.FONT_HERSHEY_COMPLEX_SMALL,
@@ -309,7 +330,10 @@ def Predict_bbox_mp(Frames_data, Predicted_data, Processing_times):
             Processing_times.put(time.time())
             
             if YOLO_FRAMEWORK == "tf":
-                pred_bbox = Yolo.predict(image_data)
+                if tf.__version__ > '2.4.0':
+                    pred_bbox = Yolo(image_data)
+                else:
+                    pred_bbox = Yolo.predict(image_data)
             elif YOLO_FRAMEWORK == "trt":
                 batched_input = tf.constant(image_data)
                 result = Yolo(batched_input)
@@ -438,7 +462,10 @@ def detect_video(Yolo, video_path, output_path, input_size=416, show=False, CLAS
 
         t1 = time.time()
         if YOLO_FRAMEWORK == "tf":
-            pred_bbox = Yolo.predict(image_data)
+            if tf.__version__ > '2.4.0':
+                pred_bbox = Yolo(image_data, training=False)
+            else:
+                pred_bbox = Yolo.predict(image_data)
         elif YOLO_FRAMEWORK == "trt":
             batched_input = tf.constant(image_data)
             result = Yolo(batched_input)
@@ -484,17 +511,18 @@ def detect_video(Yolo, video_path, output_path, input_size=416, show=False, CLAS
 # detect from webcam
 def detect_realtime(Yolo, output_path, input_size=416, show=False, CLASSES=YOLO_COCO_CLASSES, score_threshold=0.3, iou_threshold=0.45, rectangle_colors=''):
     times = []
-    vid = cv2.VideoCapture(0)
+    vid = cv2.VideoCapture(1)
 
-    # by default VideoCapture returns float instead of int
-    width = int(vid.get(cv2.CAP_PROP_FRAME_WIDTH))
-    height = int(vid.get(cv2.CAP_PROP_FRAME_HEIGHT))
-    fps = int(vid.get(cv2.CAP_PROP_FPS))
-    codec = cv2.VideoWriter_fourcc(*'XVID')
-    out = cv2.VideoWriter(output_path, codec, fps, (width, height)) # output_path must be .mp4
+    if output_path:
+        # by default VideoCapture returns float instead of int
+        width = int(vid.get(cv2.CAP_PROP_FRAME_WIDTH))
+        height = int(vid.get(cv2.CAP_PROP_FRAME_HEIGHT))
+        fps = int(vid.get(cv2.CAP_PROP_FPS))
+        codec = cv2.VideoWriter_fourcc(*'XVID')
+        out = cv2.VideoWriter(output_path, codec, fps, (width, height)) # output_path must be .mp4
 
     while True:
-        _, frame = vid.read()
+        ret, frame = vid.read()
 
         try:
             original_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
@@ -506,7 +534,12 @@ def detect_realtime(Yolo, output_path, input_size=416, show=False, CLASSES=YOLO_
 
         t1 = time.time()
         if YOLO_FRAMEWORK == "tf":
-            pred_bbox = Yolo.predict(image_data)
+            if tf.__version__ > '2.4.0':
+                pred_bbox = Yolo(image_data, training=False)
+            else:
+                pred_bbox = Yolo.predict(image_data)
+            # if True:
+            #     pred_bbox = Yolo.predict(image_data)
         elif YOLO_FRAMEWORK == "trt":
             batched_input = tf.constant(image_data)
             result = Yolo(batched_input)
diff --git a/PAR 152/Yolo Tensorflow/yolov3/yolov3.py b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/yolov3/yolov3.py
similarity index 94%
rename from PAR 152/Yolo Tensorflow/yolov3/yolov3.py
rename to PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/yolov3/yolov3.py
index f47b7eded5b9af6b07c94af2b9180e6fa7aea09f..af98d2f732431802e4a50d5e17fe08104898adc3 100644
--- a/PAR 152/Yolo Tensorflow/yolov3/yolov3.py	
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/yolov3/yolov3.py	
@@ -1,21 +1,23 @@
+#================================================================
+#
+#   File name   : yolov3.py
+#   Author      : PyLessons
+#   Created date: 2020-06-04
+#   Website     : https://pylessons.com/
+#   GitHub      : https://github.com/pythonlessons/TensorFlow-2.x-YOLOv3
+#   Description : main yolov3 functions
+#
+#================================================================
 import numpy as np
 import tensorflow as tf
 from tensorflow.keras.layers import Conv2D, Input, LeakyReLU, ZeroPadding2D, BatchNormalization, MaxPool2D
 from tensorflow.keras.regularizers import l2
+from yolov3.utils import read_class_names
 from yolov3.configs import *
 
 STRIDES         = np.array(YOLO_STRIDES)
 ANCHORS         = (np.array(YOLO_ANCHORS).T/STRIDES).T
 
-
-def read_class_names(class_file_name):
-    # loads class name from a file
-    names = {}
-    with open(class_file_name, 'r') as data:
-        for ID, name in enumerate(data):
-            names[ID] = name.strip('\n')
-    return names
-
 class BatchNormalization(BatchNormalization):
     # "Frozen state" and "inference mode" are two separate concepts.
     # `layer.trainable = False` is to freeze the layer, so the layer will use
@@ -362,23 +364,3 @@ def compute_loss(pred, conv, label, bboxes, i=0, CLASSES=YOLO_COCO_CLASSES):
     prob_loss = tf.reduce_mean(tf.reduce_sum(prob_loss, axis=[1,2,3,4]))
 
     return giou_loss, conf_loss, prob_loss
-
-def Create_Yolo(input_size=416, channels=3, training=False, CLASSES=YOLO_COCO_CLASSES):
-    NUM_CLASS = len(read_class_names(CLASSES))
-    input_layer  = Input([input_size, input_size, channels])
-
-    if TRAIN_YOLO_TINY:
-        if YOLO_TYPE == "yolov3":
-            conv_tensors = YOLOv3_tiny(input_layer, NUM_CLASS)
-    else:
-        if YOLO_TYPE == "yolov3":
-            conv_tensors = YOLOv3(input_layer, NUM_CLASS)
-
-    output_tensors = []
-    for i, conv_tensor in enumerate(conv_tensors):
-        pred_tensor = decode(conv_tensor, NUM_CLASS, i)
-        if training: output_tensors.append(conv_tensor)
-        output_tensors.append(pred_tensor)
-
-    Yolo = tf.keras.Model(input_layer, output_tensors)
-    return Yolo
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/yolov3/yolov4.py b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/yolov3/yolov4.py
new file mode 100644
index 0000000000000000000000000000000000000000..6e67fc4f3c9d7bb01f04456414cf207d0f2d53b3
--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/yolov3/yolov4.py	
@@ -0,0 +1,582 @@
+#================================================================
+#
+#   File name   : yolov4.py
+#   Author      : PyLessons
+#   Created date: 2020-09-31
+#   Website     : https://pylessons.com/
+#   GitHub      : https://github.com/pythonlessons/TensorFlow-2.x-YOLOv3
+#   Description : main yolov3 & yolov4 functions
+#
+#================================================================
+import numpy as np
+import tensorflow as tf
+from tensorflow.keras.layers import Conv2D, Input, LeakyReLU, ZeroPadding2D, BatchNormalization, MaxPool2D
+from tensorflow.keras.regularizers import l2
+from yolov3.configs import *
+
+STRIDES         = np.array(YOLO_STRIDES)
+ANCHORS         = (np.array(YOLO_ANCHORS).T/STRIDES).T
+
+def read_class_names(class_file_name):
+    # loads class name from a file
+    names = {}
+    with open(class_file_name, 'r') as data:
+        for ID, name in enumerate(data):
+            names[ID] = name.strip('\n')
+    return names
+
+class BatchNormalization(BatchNormalization):
+    # "Frozen state" and "inference mode" are two separate concepts.
+    # `layer.trainable = False` is to freeze the layer, so the layer will use
+    # stored moving `var` and `mean` in the "inference mode", and both `gama`
+    # and `beta` will not be updated !
+    def call(self, x, training=False):
+        if not training:
+            training = tf.constant(False)
+        training = tf.logical_and(training, self.trainable)
+        return super().call(x, training)
+
+def convolutional(input_layer, filters_shape, downsample=False, activate=True, bn=True, activate_type='leaky'):
+    if downsample:
+        input_layer = ZeroPadding2D(((1, 0), (1, 0)))(input_layer)
+        padding = 'valid'
+        strides = 2
+    else:
+        strides = 1
+        padding = 'same'
+
+    conv = Conv2D(filters=filters_shape[-1], kernel_size = filters_shape[0], strides=strides,
+                  padding=padding, use_bias=not bn, kernel_regularizer=l2(0.0005),
+                  kernel_initializer=tf.random_normal_initializer(stddev=0.01),
+                  bias_initializer=tf.constant_initializer(0.))(input_layer)
+    if bn:
+        conv = BatchNormalization()(conv)
+    if activate == True:
+        if activate_type == "leaky":
+            conv = LeakyReLU(alpha=0.1)(conv)
+        elif activate_type == "mish":
+            conv = mish(conv)
+
+    return conv
+
+def mish(x):
+    return x * tf.math.tanh(tf.math.softplus(x))
+
+def residual_block(input_layer, input_channel, filter_num1, filter_num2, activate_type='leaky'):
+    short_cut = input_layer
+    conv = convolutional(input_layer, filters_shape=(1, 1, input_channel, filter_num1), activate_type=activate_type)
+    conv = convolutional(conv       , filters_shape=(3, 3, filter_num1,   filter_num2), activate_type=activate_type)
+
+    residual_output = short_cut + conv
+    return residual_output
+
+def upsample(input_layer):
+    return tf.image.resize(input_layer, (input_layer.shape[1] * 2, input_layer.shape[2] * 2), method='nearest')
+
+def route_group(input_layer, groups, group_id):
+    convs = tf.split(input_layer, num_or_size_splits=groups, axis=-1)
+    return convs[group_id]
+
+def darknet53(input_data):
+    input_data = convolutional(input_data, (3, 3,  3,  32))
+    input_data = convolutional(input_data, (3, 3, 32,  64), downsample=True)
+
+    for i in range(1):
+        input_data = residual_block(input_data,  64,  32, 64)
+
+    input_data = convolutional(input_data, (3, 3,  64, 128), downsample=True)
+
+    for i in range(2):
+        input_data = residual_block(input_data, 128,  64, 128)
+
+    input_data = convolutional(input_data, (3, 3, 128, 256), downsample=True)
+
+    for i in range(8):
+        input_data = residual_block(input_data, 256, 128, 256)
+
+    route_1 = input_data
+    input_data = convolutional(input_data, (3, 3, 256, 512), downsample=True)
+
+    for i in range(8):
+        input_data = residual_block(input_data, 512, 256, 512)
+
+    route_2 = input_data
+    input_data = convolutional(input_data, (3, 3, 512, 1024), downsample=True)
+
+    for i in range(4):
+        input_data = residual_block(input_data, 1024, 512, 1024)
+
+    return route_1, route_2, input_data
+
+def cspdarknet53(input_data):
+    input_data = convolutional(input_data, (3, 3,  3,  32), activate_type="mish")
+    input_data = convolutional(input_data, (3, 3, 32,  64), downsample=True, activate_type="mish")
+
+    route = input_data
+    route = convolutional(route, (1, 1, 64, 64), activate_type="mish")
+    input_data = convolutional(input_data, (1, 1, 64, 64), activate_type="mish")
+    for i in range(1):
+        input_data = residual_block(input_data,  64,  32, 64, activate_type="mish")
+    input_data = convolutional(input_data, (1, 1, 64, 64), activate_type="mish")
+
+    input_data = tf.concat([input_data, route], axis=-1)
+    input_data = convolutional(input_data, (1, 1, 128, 64), activate_type="mish")
+    input_data = convolutional(input_data, (3, 3, 64, 128), downsample=True, activate_type="mish")
+    route = input_data
+    route = convolutional(route, (1, 1, 128, 64), activate_type="mish")
+    input_data = convolutional(input_data, (1, 1, 128, 64), activate_type="mish")
+    for i in range(2):
+        input_data = residual_block(input_data, 64,  64, 64, activate_type="mish")
+    input_data = convolutional(input_data, (1, 1, 64, 64), activate_type="mish")
+    input_data = tf.concat([input_data, route], axis=-1)
+
+    input_data = convolutional(input_data, (1, 1, 128, 128), activate_type="mish")
+    input_data = convolutional(input_data, (3, 3, 128, 256), downsample=True, activate_type="mish")
+    route = input_data
+    route = convolutional(route, (1, 1, 256, 128), activate_type="mish")
+    input_data = convolutional(input_data, (1, 1, 256, 128), activate_type="mish")
+    for i in range(8):
+        input_data = residual_block(input_data, 128, 128, 128, activate_type="mish")
+    input_data = convolutional(input_data, (1, 1, 128, 128), activate_type="mish")
+    input_data = tf.concat([input_data, route], axis=-1)
+
+    input_data = convolutional(input_data, (1, 1, 256, 256), activate_type="mish")
+    route_1 = input_data
+    input_data = convolutional(input_data, (3, 3, 256, 512), downsample=True, activate_type="mish")
+    route = input_data
+    route = convolutional(route, (1, 1, 512, 256), activate_type="mish")
+    input_data = convolutional(input_data, (1, 1, 512, 256), activate_type="mish")
+    for i in range(8):
+        input_data = residual_block(input_data, 256, 256, 256, activate_type="mish")
+    input_data = convolutional(input_data, (1, 1, 256, 256), activate_type="mish")
+    input_data = tf.concat([input_data, route], axis=-1)
+
+    input_data = convolutional(input_data, (1, 1, 512, 512), activate_type="mish")
+    route_2 = input_data
+    input_data = convolutional(input_data, (3, 3, 512, 1024), downsample=True, activate_type="mish")
+    route = input_data
+    route = convolutional(route, (1, 1, 1024, 512), activate_type="mish")
+    input_data = convolutional(input_data, (1, 1, 1024, 512), activate_type="mish")
+    for i in range(4):
+        input_data = residual_block(input_data, 512, 512, 512, activate_type="mish")
+    input_data = convolutional(input_data, (1, 1, 512, 512), activate_type="mish")
+    input_data = tf.concat([input_data, route], axis=-1)
+
+    input_data = convolutional(input_data, (1, 1, 1024, 1024), activate_type="mish")
+    input_data = convolutional(input_data, (1, 1, 1024, 512))
+    input_data = convolutional(input_data, (3, 3, 512, 1024))
+    input_data = convolutional(input_data, (1, 1, 1024, 512))
+
+    max_pooling_1 = tf.keras.layers.MaxPool2D(pool_size=13, padding='SAME', strides=1)(input_data)
+    max_pooling_2 = tf.keras.layers.MaxPool2D(pool_size=9, padding='SAME', strides=1)(input_data)
+    max_pooling_3 = tf.keras.layers.MaxPool2D(pool_size=5, padding='SAME', strides=1)(input_data)
+    input_data = tf.concat([max_pooling_1, max_pooling_2, max_pooling_3, input_data], axis=-1)
+
+    input_data = convolutional(input_data, (1, 1, 2048, 512))
+    input_data = convolutional(input_data, (3, 3, 512, 1024))
+    input_data = convolutional(input_data, (1, 1, 1024, 512))
+
+    return route_1, route_2, input_data
+
+def darknet19_tiny(input_data):
+    input_data = convolutional(input_data, (3, 3, 3, 16))
+    input_data = MaxPool2D(2, 2, 'same')(input_data)
+    input_data = convolutional(input_data, (3, 3, 16, 32))
+    input_data = MaxPool2D(2, 2, 'same')(input_data)
+    input_data = convolutional(input_data, (3, 3, 32, 64))
+    input_data = MaxPool2D(2, 2, 'same')(input_data)
+    input_data = convolutional(input_data, (3, 3, 64, 128))
+    input_data = MaxPool2D(2, 2, 'same')(input_data)
+    input_data = convolutional(input_data, (3, 3, 128, 256))
+    route_1 = input_data
+    input_data = MaxPool2D(2, 2, 'same')(input_data)
+    input_data = convolutional(input_data, (3, 3, 256, 512))
+    input_data = MaxPool2D(2, 1, 'same')(input_data)
+    input_data = convolutional(input_data, (3, 3, 512, 1024))
+
+    return route_1, input_data
+
+def cspdarknet53_tiny(input_data): # not sure how this should be called
+    input_data = convolutional(input_data, (3, 3, 3, 32), downsample=True)
+    input_data = convolutional(input_data, (3, 3, 32, 64), downsample=True)
+    input_data = convolutional(input_data, (3, 3, 64, 64))
+
+    route = input_data
+    input_data = route_group(input_data, 2, 1)
+    input_data = convolutional(input_data, (3, 3, 32, 32))
+    route_1 = input_data
+    input_data = convolutional(input_data, (3, 3, 32, 32))
+    input_data = tf.concat([input_data, route_1], axis=-1)
+    input_data = convolutional(input_data, (1, 1, 32, 64))
+    input_data = tf.concat([route, input_data], axis=-1)
+    input_data = MaxPool2D(2, 2, 'same')(input_data)
+
+    input_data = convolutional(input_data, (3, 3, 64, 128))
+    route = input_data
+    input_data = route_group(input_data, 2, 1)
+    input_data = convolutional(input_data, (3, 3, 64, 64))
+    route_1 = input_data
+    input_data = convolutional(input_data, (3, 3, 64, 64))
+    input_data = tf.concat([input_data, route_1], axis=-1)
+    input_data = convolutional(input_data, (1, 1, 64, 128))
+    input_data = tf.concat([route, input_data], axis=-1)
+    input_data = MaxPool2D(2, 2, 'same')(input_data)
+
+    input_data = convolutional(input_data, (3, 3, 128, 256))
+    route = input_data
+    input_data = route_group(input_data, 2, 1)
+    input_data = convolutional(input_data, (3, 3, 128, 128))
+    route_1 = input_data
+    input_data = convolutional(input_data, (3, 3, 128, 128))
+    input_data = tf.concat([input_data, route_1], axis=-1)
+    input_data = convolutional(input_data, (1, 1, 128, 256))
+    route_1 = input_data
+    input_data = tf.concat([route, input_data], axis=-1)
+    input_data = MaxPool2D(2, 2, 'same')(input_data)
+
+    input_data = convolutional(input_data, (3, 3, 512, 512))
+
+    return route_1, input_data
+
+def YOLOv3(input_layer, NUM_CLASS):
+    # After the input layer enters the Darknet-53 network, we get three branches
+    route_1, route_2, conv = darknet53(input_layer)
+    # See the orange module (DBL) in the figure above, a total of 5 Subconvolution operation
+    conv = convolutional(conv, (1, 1, 1024,  512))
+    conv = convolutional(conv, (3, 3,  512, 1024))
+    conv = convolutional(conv, (1, 1, 1024,  512))
+    conv = convolutional(conv, (3, 3,  512, 1024))
+    conv = convolutional(conv, (1, 1, 1024,  512))
+    conv_lobj_branch = convolutional(conv, (3, 3, 512, 1024))
+    
+    # conv_lbbox is used to predict large-sized objects , Shape = [None, 13, 13, 255] 
+    conv_lbbox = convolutional(conv_lobj_branch, (1, 1, 1024, 3*(NUM_CLASS + 5)), activate=False, bn=False)
+
+    conv = convolutional(conv, (1, 1,  512,  256))
+    # upsample here uses the nearest neighbor interpolation method, which has the advantage that the
+    # upsampling process does not need to learn, thereby reducing the network parameter  
+    conv = upsample(conv)
+
+    conv = tf.concat([conv, route_2], axis=-1)
+    conv = convolutional(conv, (1, 1, 768, 256))
+    conv = convolutional(conv, (3, 3, 256, 512))
+    conv = convolutional(conv, (1, 1, 512, 256))
+    conv = convolutional(conv, (3, 3, 256, 512))
+    conv = convolutional(conv, (1, 1, 512, 256))
+    conv_mobj_branch = convolutional(conv, (3, 3, 256, 512))
+
+    # conv_mbbox is used to predict medium-sized objects, shape = [None, 26, 26, 255]
+    conv_mbbox = convolutional(conv_mobj_branch, (1, 1, 512, 3*(NUM_CLASS + 5)), activate=False, bn=False)
+
+    conv = convolutional(conv, (1, 1, 256, 128))
+    conv = upsample(conv)
+
+    conv = tf.concat([conv, route_1], axis=-1)
+    conv = convolutional(conv, (1, 1, 384, 128))
+    conv = convolutional(conv, (3, 3, 128, 256))
+    conv = convolutional(conv, (1, 1, 256, 128))
+    conv = convolutional(conv, (3, 3, 128, 256))
+    conv = convolutional(conv, (1, 1, 256, 128))
+    conv_sobj_branch = convolutional(conv, (3, 3, 128, 256))
+    
+    # conv_sbbox is used to predict small size objects, shape = [None, 52, 52, 255]
+    conv_sbbox = convolutional(conv_sobj_branch, (1, 1, 256, 3*(NUM_CLASS +5)), activate=False, bn=False)
+        
+    return [conv_sbbox, conv_mbbox, conv_lbbox]
+
+def YOLOv4(input_layer, NUM_CLASS):
+    route_1, route_2, conv = cspdarknet53(input_layer)
+
+    route = conv
+    conv = convolutional(conv, (1, 1, 512, 256))
+    conv = upsample(conv)
+    route_2 = convolutional(route_2, (1, 1, 512, 256))
+    conv = tf.concat([route_2, conv], axis=-1)
+
+    conv = convolutional(conv, (1, 1, 512, 256))
+    conv = convolutional(conv, (3, 3, 256, 512))
+    conv = convolutional(conv, (1, 1, 512, 256))
+    conv = convolutional(conv, (3, 3, 256, 512))
+    conv = convolutional(conv, (1, 1, 512, 256))
+
+    route_2 = conv
+    conv = convolutional(conv, (1, 1, 256, 128))
+    conv = upsample(conv)
+    route_1 = convolutional(route_1, (1, 1, 256, 128))
+    conv = tf.concat([route_1, conv], axis=-1)
+
+    conv = convolutional(conv, (1, 1, 256, 128))
+    conv = convolutional(conv, (3, 3, 128, 256))
+    conv = convolutional(conv, (1, 1, 256, 128))
+    conv = convolutional(conv, (3, 3, 128, 256))
+    conv = convolutional(conv, (1, 1, 256, 128))
+
+    route_1 = conv
+    conv = convolutional(conv, (3, 3, 128, 256))
+    conv_sbbox = convolutional(conv, (1, 1, 256, 3 * (NUM_CLASS + 5)), activate=False, bn=False)
+
+    conv = convolutional(route_1, (3, 3, 128, 256), downsample=True)
+    conv = tf.concat([conv, route_2], axis=-1)
+
+    conv = convolutional(conv, (1, 1, 512, 256))
+    conv = convolutional(conv, (3, 3, 256, 512))
+    conv = convolutional(conv, (1, 1, 512, 256))
+    conv = convolutional(conv, (3, 3, 256, 512))
+    conv = convolutional(conv, (1, 1, 512, 256))
+
+    route_2 = conv
+    conv = convolutional(conv, (3, 3, 256, 512))
+    conv_mbbox = convolutional(conv, (1, 1, 512, 3 * (NUM_CLASS + 5)), activate=False, bn=False)
+
+    conv = convolutional(route_2, (3, 3, 256, 512), downsample=True)
+    conv = tf.concat([conv, route], axis=-1)
+
+    conv = convolutional(conv, (1, 1, 1024, 512))
+    conv = convolutional(conv, (3, 3, 512, 1024))
+    conv = convolutional(conv, (1, 1, 1024, 512))
+    conv = convolutional(conv, (3, 3, 512, 1024))
+    conv = convolutional(conv, (1, 1, 1024, 512))
+
+    conv = convolutional(conv, (3, 3, 512, 1024))
+    conv_lbbox = convolutional(conv, (1, 1, 1024, 3 * (NUM_CLASS + 5)), activate=False, bn=False)
+
+    return [conv_sbbox, conv_mbbox, conv_lbbox]
+
+def YOLOv3_tiny(input_layer, NUM_CLASS):
+    # After the input layer enters the Darknet-53 network, we get three branches
+    route_1, conv = darknet19_tiny(input_layer)
+
+    conv = convolutional(conv, (1, 1, 1024, 256))
+    conv_lobj_branch = convolutional(conv, (3, 3, 256, 512))
+    
+    # conv_lbbox is used to predict large-sized objects , Shape = [None, 26, 26, 255]
+    conv_lbbox = convolutional(conv_lobj_branch, (1, 1, 512, 3*(NUM_CLASS + 5)), activate=False, bn=False)
+
+    conv = convolutional(conv, (1, 1, 256, 128))
+    # upsample here uses the nearest neighbor interpolation method, which has the advantage that the
+    # upsampling process does not need to learn, thereby reducing the network parameter  
+    conv = upsample(conv)
+    
+    conv = tf.concat([conv, route_1], axis=-1)
+    conv_mobj_branch = convolutional(conv, (3, 3, 128, 256))
+    # conv_mbbox is used to predict medium size objects, shape = [None, 13, 13, 255]
+    conv_mbbox = convolutional(conv_mobj_branch, (1, 1, 256, 3 * (NUM_CLASS + 5)), activate=False, bn=False)
+
+    return [conv_mbbox, conv_lbbox]
+
+def YOLOv4_tiny(input_layer, NUM_CLASS):
+    route_1, conv = cspdarknet53_tiny(input_layer)
+
+    conv = convolutional(conv, (1, 1, 512, 256))
+
+    conv_lobj_branch = convolutional(conv, (3, 3, 256, 512))
+    conv_lbbox = convolutional(conv_lobj_branch, (1, 1, 512, 3 * (NUM_CLASS + 5)), activate=False, bn=False)
+
+    conv = convolutional(conv, (1, 1, 256, 128))
+    conv = upsample(conv)
+    conv = tf.concat([conv, route_1], axis=-1)
+
+    conv_mobj_branch = convolutional(conv, (3, 3, 128, 256))
+    conv_mbbox = convolutional(conv_mobj_branch, (1, 1, 256, 3 * (NUM_CLASS + 5)), activate=False, bn=False)
+
+    return [conv_mbbox, conv_lbbox]
+
+def Create_Yolo(input_size=416, channels=3, training=False, CLASSES=YOLO_COCO_CLASSES):
+    NUM_CLASS = len(read_class_names(CLASSES))
+    input_layer  = Input([input_size, input_size, channels])
+
+    if TRAIN_YOLO_TINY:
+        if YOLO_TYPE == "yolov4":
+            conv_tensors = YOLOv4_tiny(input_layer, NUM_CLASS)
+        if YOLO_TYPE == "yolov3":
+            conv_tensors = YOLOv3_tiny(input_layer, NUM_CLASS)
+    else:
+        if YOLO_TYPE == "yolov4":
+            conv_tensors = YOLOv4(input_layer, NUM_CLASS)
+        if YOLO_TYPE == "yolov3":
+            conv_tensors = YOLOv3(input_layer, NUM_CLASS)
+
+    output_tensors = []
+    for i, conv_tensor in enumerate(conv_tensors):
+        pred_tensor = decode(conv_tensor, NUM_CLASS, i)
+        if training: output_tensors.append(conv_tensor)
+        output_tensors.append(pred_tensor)
+
+    Yolo = tf.keras.Model(input_layer, output_tensors)
+    return Yolo
+
+
+def decode(conv_output, NUM_CLASS, i=0):
+    # where i = 0, 1 or 2 to correspond to the three grid scales  
+    conv_shape       = tf.shape(conv_output)
+    batch_size       = conv_shape[0]
+    output_size      = conv_shape[1]
+
+    conv_output = tf.reshape(conv_output, (batch_size, output_size, output_size, 3, 5 + NUM_CLASS))
+
+    #conv_raw_dxdy = conv_output[:, :, :, :, 0:2] # offset of center position     
+    #conv_raw_dwdh = conv_output[:, :, :, :, 2:4] # Prediction box length and width offset
+    #conv_raw_conf = conv_output[:, :, :, :, 4:5] # confidence of the prediction box
+    #conv_raw_prob = conv_output[:, :, :, :, 5: ] # category probability of the prediction box
+    conv_raw_dxdy, conv_raw_dwdh, conv_raw_conf, conv_raw_prob = tf.split(conv_output, (2, 2, 1, NUM_CLASS), axis=-1)
+
+    # next need Draw the grid. Where output_size is equal to 13, 26 or 52  
+    #y = tf.range(output_size, dtype=tf.int32)
+    #y = tf.expand_dims(y, -1)
+    #y = tf.tile(y, [1, output_size])
+    #x = tf.range(output_size,dtype=tf.int32)
+    #x = tf.expand_dims(x, 0)
+    #x = tf.tile(x, [output_size, 1])
+    xy_grid = tf.meshgrid(tf.range(output_size), tf.range(output_size))
+    xy_grid = tf.expand_dims(tf.stack(xy_grid, axis=-1), axis=2)  # [gx, gy, 1, 2]
+    xy_grid = tf.tile(tf.expand_dims(xy_grid, axis=0), [batch_size, 1, 1, 3, 1])
+    xy_grid = tf.cast(xy_grid, tf.float32)
+    
+    #xy_grid = tf.concat([x[:, :, tf.newaxis], y[:, :, tf.newaxis]], axis=-1)
+    #xy_grid = tf.tile(xy_grid[tf.newaxis, :, :, tf.newaxis, :], [batch_size, 1, 1, 3, 1])
+    #y_grid = tf.cast(xy_grid, tf.float32)
+
+    # Calculate the center position of the prediction box:
+    pred_xy = (tf.sigmoid(conv_raw_dxdy) + xy_grid) * STRIDES[i]
+    # Calculate the length and width of the prediction box:
+    pred_wh = (tf.exp(conv_raw_dwdh) * ANCHORS[i]) * STRIDES[i]
+
+    pred_xywh = tf.concat([pred_xy, pred_wh], axis=-1)
+    pred_conf = tf.sigmoid(conv_raw_conf) # object box calculates the predicted confidence
+    pred_prob = tf.sigmoid(conv_raw_prob) # calculating the predicted probability category box object
+
+    # calculating the predicted probability category box object
+    return tf.concat([pred_xywh, pred_conf, pred_prob], axis=-1)
+
+
+def bbox_iou(boxes1, boxes2):
+    boxes1_area = boxes1[..., 2] * boxes1[..., 3]
+    boxes2_area = boxes2[..., 2] * boxes2[..., 3]
+
+    boxes1 = tf.concat([boxes1[..., :2] - boxes1[..., 2:] * 0.5,
+                        boxes1[..., :2] + boxes1[..., 2:] * 0.5], axis=-1)
+    boxes2 = tf.concat([boxes2[..., :2] - boxes2[..., 2:] * 0.5,
+                        boxes2[..., :2] + boxes2[..., 2:] * 0.5], axis=-1)
+
+    left_up = tf.maximum(boxes1[..., :2], boxes2[..., :2])
+    right_down = tf.minimum(boxes1[..., 2:], boxes2[..., 2:])
+
+    inter_section = tf.maximum(right_down - left_up, 0.0)
+    inter_area = inter_section[..., 0] * inter_section[..., 1]
+    union_area = boxes1_area + boxes2_area - inter_area
+
+    return 1.0 * inter_area / union_area
+
+def bbox_giou(boxes1, boxes2):
+    boxes1 = tf.concat([boxes1[..., :2] - boxes1[..., 2:] * 0.5,
+                        boxes1[..., :2] + boxes1[..., 2:] * 0.5], axis=-1)
+    boxes2 = tf.concat([boxes2[..., :2] - boxes2[..., 2:] * 0.5,
+                        boxes2[..., :2] + boxes2[..., 2:] * 0.5], axis=-1)
+
+    boxes1 = tf.concat([tf.minimum(boxes1[..., :2], boxes1[..., 2:]),
+                        tf.maximum(boxes1[..., :2], boxes1[..., 2:])], axis=-1)
+    boxes2 = tf.concat([tf.minimum(boxes2[..., :2], boxes2[..., 2:]),
+                        tf.maximum(boxes2[..., :2], boxes2[..., 2:])], axis=-1)
+
+    boxes1_area = (boxes1[..., 2] - boxes1[..., 0]) * (boxes1[..., 3] - boxes1[..., 1])
+    boxes2_area = (boxes2[..., 2] - boxes2[..., 0]) * (boxes2[..., 3] - boxes2[..., 1])
+
+    left_up = tf.maximum(boxes1[..., :2], boxes2[..., :2])
+    right_down = tf.minimum(boxes1[..., 2:], boxes2[..., 2:])
+
+    inter_section = tf.maximum(right_down - left_up, 0.0)
+    inter_area = inter_section[..., 0] * inter_section[..., 1]
+    union_area = boxes1_area + boxes2_area - inter_area
+
+    # Calculate the iou value between the two bounding boxes
+    iou = inter_area / union_area
+
+    # Calculate the coordinates of the upper left corner and the lower right corner of the smallest closed convex surface
+    enclose_left_up = tf.minimum(boxes1[..., :2], boxes2[..., :2])
+    enclose_right_down = tf.maximum(boxes1[..., 2:], boxes2[..., 2:])
+    enclose = tf.maximum(enclose_right_down - enclose_left_up, 0.0)
+
+    # Calculate the area of the smallest closed convex surface C
+    enclose_area = enclose[..., 0] * enclose[..., 1]
+
+    # Calculate the GIoU value according to the GioU formula  
+    giou = iou - 1.0 * (enclose_area - union_area) / enclose_area
+
+    return giou
+
+# testing (should be better than giou)
+def bbox_ciou(boxes1, boxes2):
+    boxes1_coor = tf.concat([boxes1[..., :2] - boxes1[..., 2:] * 0.5,
+                        boxes1[..., :2] + boxes1[..., 2:] * 0.5], axis=-1)
+    boxes2_coor = tf.concat([boxes2[..., :2] - boxes2[..., 2:] * 0.5,
+                        boxes2[..., :2] + boxes2[..., 2:] * 0.5], axis=-1)
+
+    left = tf.maximum(boxes1_coor[..., 0], boxes2_coor[..., 0])
+    up = tf.maximum(boxes1_coor[..., 1], boxes2_coor[..., 1])
+    right = tf.maximum(boxes1_coor[..., 2], boxes2_coor[..., 2])
+    down = tf.maximum(boxes1_coor[..., 3], boxes2_coor[..., 3])
+
+    c = (right - left) * (right - left) + (up - down) * (up - down)
+    iou = bbox_iou(boxes1, boxes2)
+
+    u = (boxes1[..., 0] - boxes2[..., 0]) * (boxes1[..., 0] - boxes2[..., 0]) + (boxes1[..., 1] - boxes2[..., 1]) * (boxes1[..., 1] - boxes2[..., 1])
+    d = u / c
+
+    ar_gt = boxes2[..., 2] / boxes2[..., 3]
+    ar_pred = boxes1[..., 2] / boxes1[..., 3]
+
+    ar_loss = 4 / (np.pi * np.pi) * (tf.atan(ar_gt) - tf.atan(ar_pred)) * (tf.atan(ar_gt) - tf.atan(ar_pred))
+    alpha = ar_loss / (1 - iou + ar_loss + 0.000001)
+    ciou_term = d + alpha * ar_loss
+
+    return iou - ciou_term
+
+
+def compute_loss(pred, conv, label, bboxes, i=0, CLASSES=YOLO_COCO_CLASSES):
+    NUM_CLASS = len(read_class_names(CLASSES))
+    conv_shape  = tf.shape(conv)
+    batch_size  = conv_shape[0]
+    output_size = conv_shape[1]
+    input_size  = STRIDES[i] * output_size
+    conv = tf.reshape(conv, (batch_size, output_size, output_size, 3, 5 + NUM_CLASS))
+
+    conv_raw_conf = conv[:, :, :, :, 4:5]
+    conv_raw_prob = conv[:, :, :, :, 5:]
+
+    pred_xywh     = pred[:, :, :, :, 0:4]
+    pred_conf     = pred[:, :, :, :, 4:5]
+
+    label_xywh    = label[:, :, :, :, 0:4]
+    respond_bbox  = label[:, :, :, :, 4:5]
+    label_prob    = label[:, :, :, :, 5:]
+
+    giou = tf.expand_dims(bbox_giou(pred_xywh, label_xywh), axis=-1)
+    input_size = tf.cast(input_size, tf.float32)
+
+    bbox_loss_scale = 2.0 - 1.0 * label_xywh[:, :, :, :, 2:3] * label_xywh[:, :, :, :, 3:4] / (input_size ** 2)
+    giou_loss = respond_bbox * bbox_loss_scale * (1 - giou)
+
+    iou = bbox_iou(pred_xywh[:, :, :, :, np.newaxis, :], bboxes[:, np.newaxis, np.newaxis, np.newaxis, :, :])
+    # Find the value of IoU with the real box The largest prediction box
+    max_iou = tf.expand_dims(tf.reduce_max(iou, axis=-1), axis=-1)
+
+    # If the largest iou is less than the threshold, it is considered that the prediction box contains no objects, then the background box
+    respond_bgd = (1.0 - respond_bbox) * tf.cast( max_iou < YOLO_IOU_LOSS_THRESH, tf.float32 )
+
+    conf_focal = tf.pow(respond_bbox - pred_conf, 2)
+
+    # Calculate the loss of confidence
+    # we hope that if the grid contains objects, then the network output prediction box has a confidence of 1 and 0 when there is no object.
+    conf_loss = conf_focal * (
+            respond_bbox * tf.nn.sigmoid_cross_entropy_with_logits(labels=respond_bbox, logits=conv_raw_conf)
+            +
+            respond_bgd * tf.nn.sigmoid_cross_entropy_with_logits(labels=respond_bbox, logits=conv_raw_conf)
+    )
+
+    prob_loss = respond_bbox * tf.nn.sigmoid_cross_entropy_with_logits(labels=label_prob, logits=conv_raw_prob)
+
+    giou_loss = tf.reduce_mean(tf.reduce_sum(giou_loss, axis=[1,2,3,4]))
+    conf_loss = tf.reduce_mean(tf.reduce_sum(conf_loss, axis=[1,2,3,4]))
+    prob_loss = tf.reduce_mean(tf.reduce_sum(prob_loss, axis=[1,2,3,4]))
+
+    return giou_loss, conf_loss, prob_loss
diff --git a/PAR 152/cone_dataset/1.jpg b/PAR 152/cone_dataset/1.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..01ebe69cad036a034263da538170207c0e9c6ce4
Binary files /dev/null and b/PAR 152/cone_dataset/1.jpg differ
diff --git a/PAR 152/cone_dataset/1.xml b/PAR 152/cone_dataset/1.xml
new file mode 100644
index 0000000000000000000000000000000000000000..d159c342a9eff91ecfc2b2bff46ab2af8237db2f
--- /dev/null
+++ b/PAR 152/cone_dataset/1.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>1.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\1.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>414</width>
+		<height>414</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>112</xmin>
+			<ymin>35</ymin>
+			<xmax>309</xmax>
+			<ymax>327</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/10.jpg b/PAR 152/cone_dataset/10.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..a02e00027b0ae41f3f9ba675604cd3ad057d2ace
Binary files /dev/null and b/PAR 152/cone_dataset/10.jpg differ
diff --git a/PAR 152/cone_dataset/10.xml b/PAR 152/cone_dataset/10.xml
new file mode 100644
index 0000000000000000000000000000000000000000..97cce271f9014d863bade26e13d8ffe88d572943
--- /dev/null
+++ b/PAR 152/cone_dataset/10.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>10.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\10.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>508</width>
+		<height>337</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>248</xmin>
+			<ymin>39</ymin>
+			<xmax>436</xmax>
+			<ymax>328</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/100.jpg b/PAR 152/cone_dataset/100.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..80c8338e7769becad871547d903d316ff4626a90
Binary files /dev/null and b/PAR 152/cone_dataset/100.jpg differ
diff --git a/PAR 152/cone_dataset/100.xml b/PAR 152/cone_dataset/100.xml
new file mode 100644
index 0000000000000000000000000000000000000000..a4f1e281d26fbb19a991c5b4a2aec1439a86652f
--- /dev/null
+++ b/PAR 152/cone_dataset/100.xml	
@@ -0,0 +1,62 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>100.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\100.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>509</width>
+		<height>339</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>40</xmin>
+			<ymin>66</ymin>
+			<xmax>229</xmax>
+			<ymax>319</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>197</xmin>
+			<ymin>73</ymin>
+			<xmax>312</xmax>
+			<ymax>248</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>323</xmin>
+			<ymin>76</ymin>
+			<xmax>387</xmax>
+			<ymax>171</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>174</xmin>
+			<ymin>80</ymin>
+			<xmax>243</xmax>
+			<ymax>201</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/101.jpg b/PAR 152/cone_dataset/101.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..59bed827780f4c68a9e708dacacdf257cb0a129f
Binary files /dev/null and b/PAR 152/cone_dataset/101.jpg differ
diff --git a/PAR 152/cone_dataset/101.xml b/PAR 152/cone_dataset/101.xml
new file mode 100644
index 0000000000000000000000000000000000000000..958c11046fa5c40a05c221f6776edf732e33ef6a
--- /dev/null
+++ b/PAR 152/cone_dataset/101.xml	
@@ -0,0 +1,62 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>101.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\101.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>336</width>
+		<height>509</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>242</xmin>
+			<ymin>383</ymin>
+			<xmax>267</xmax>
+			<ymax>436</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>171</xmin>
+			<ymin>377</ymin>
+			<xmax>213</xmax>
+			<ymax>437</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>107</xmin>
+			<ymin>381</ymin>
+			<xmax>143</xmax>
+			<ymax>434</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>32</xmin>
+			<ymin>378</ymin>
+			<xmax>73</xmax>
+			<ymax>436</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/102.jpg b/PAR 152/cone_dataset/102.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..eecc32834e0367d99fbee310b49f4a50dfe5505f
Binary files /dev/null and b/PAR 152/cone_dataset/102.jpg differ
diff --git a/PAR 152/cone_dataset/102.xml b/PAR 152/cone_dataset/102.xml
new file mode 100644
index 0000000000000000000000000000000000000000..0b575db6936b0321e789d26d19ac36ba7a6ff930
--- /dev/null
+++ b/PAR 152/cone_dataset/102.xml	
@@ -0,0 +1,74 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>102.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\102.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>359</width>
+		<height>479</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>190</xmin>
+			<ymin>275</ymin>
+			<xmax>295</xmax>
+			<ymax>430</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>212</xmin>
+			<ymin>260</ymin>
+			<xmax>297</xmax>
+			<ymax>384</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>234</xmin>
+			<ymin>234</ymin>
+			<xmax>305</xmax>
+			<ymax>345</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>264</xmin>
+			<ymin>226</ymin>
+			<xmax>324</xmax>
+			<ymax>319</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>283</xmin>
+			<ymin>214</ymin>
+			<xmax>326</xmax>
+			<ymax>289</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/103.jpg b/PAR 152/cone_dataset/103.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..ee5237156bfe91c2ca8d3b9befc3cc21ac68fcef
Binary files /dev/null and b/PAR 152/cone_dataset/103.jpg differ
diff --git a/PAR 152/cone_dataset/103.xml b/PAR 152/cone_dataset/103.xml
new file mode 100644
index 0000000000000000000000000000000000000000..c717ab0e7d044d5ff6b1ef6360c90f5f50017f4a
--- /dev/null
+++ b/PAR 152/cone_dataset/103.xml	
@@ -0,0 +1,62 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>103.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\103.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>525</width>
+		<height>329</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>124</xmin>
+			<ymin>127</ymin>
+			<xmax>228</xmax>
+			<ymax>296</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>6</xmin>
+			<ymin>69</ymin>
+			<xmax>93</xmax>
+			<ymax>194</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>255</xmin>
+			<ymin>36</ymin>
+			<xmax>321</xmax>
+			<ymax>145</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>423</xmin>
+			<ymin>81</ymin>
+			<xmax>519</xmax>
+			<ymax>223</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/104.jpg b/PAR 152/cone_dataset/104.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..3852ad3e3fea4f14901a042cf27b8735d996fb66
Binary files /dev/null and b/PAR 152/cone_dataset/104.jpg differ
diff --git a/PAR 152/cone_dataset/104.xml b/PAR 152/cone_dataset/104.xml
new file mode 100644
index 0000000000000000000000000000000000000000..1c2a4ab2cd64de1abb20dbe9000820543b202a39
--- /dev/null
+++ b/PAR 152/cone_dataset/104.xml	
@@ -0,0 +1,62 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>104.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\104.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>449</width>
+		<height>382</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>127</xmin>
+			<ymin>71</ymin>
+			<xmax>261</xmax>
+			<ymax>300</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>70</xmin>
+			<ymin>96</ymin>
+			<xmax>168</xmax>
+			<ymax>288</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>281</xmin>
+			<ymin>31</ymin>
+			<xmax>449</xmax>
+			<ymax>338</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>255</xmin>
+			<ymin>87</ymin>
+			<xmax>350</xmax>
+			<ymax>289</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/105.jpg b/PAR 152/cone_dataset/105.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..b4f6c262b920a13fb34a72919827d7a23a7d931f
Binary files /dev/null and b/PAR 152/cone_dataset/105.jpg differ
diff --git a/PAR 152/cone_dataset/105.xml b/PAR 152/cone_dataset/105.xml
new file mode 100644
index 0000000000000000000000000000000000000000..b19e2dba6aef0ea08aa7a2701e7dd202bf9a830b
--- /dev/null
+++ b/PAR 152/cone_dataset/105.xml	
@@ -0,0 +1,86 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>105.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\105.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>445</width>
+		<height>594</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>1</xmin>
+			<ymin>57</ymin>
+			<xmax>182</xmax>
+			<ymax>569</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>44</xmin>
+			<ymin>53</ymin>
+			<xmax>237</xmax>
+			<ymax>479</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>133</xmin>
+			<ymin>66</ymin>
+			<xmax>296</xmax>
+			<ymax>409</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>204</xmin>
+			<ymin>62</ymin>
+			<xmax>333</xmax>
+			<ymax>364</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>277</xmin>
+			<ymin>43</ymin>
+			<xmax>397</xmax>
+			<ymax>315</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>330</xmin>
+			<ymin>33</ymin>
+			<xmax>429</xmax>
+			<ymax>275</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/106.jpg b/PAR 152/cone_dataset/106.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..90f1137c3fa01883e90e2e3a1aced2834b8a3621
Binary files /dev/null and b/PAR 152/cone_dataset/106.jpg differ
diff --git a/PAR 152/cone_dataset/106.xml b/PAR 152/cone_dataset/106.xml
new file mode 100644
index 0000000000000000000000000000000000000000..e0d0731395371c618affdf81c108809c4e1ad1fb
--- /dev/null
+++ b/PAR 152/cone_dataset/106.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>106.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\106.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>170</width>
+		<height>108</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>5</xmin>
+			<ymin>23</ymin>
+			<xmax>102</xmax>
+			<ymax>86</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/107.jpg b/PAR 152/cone_dataset/107.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..cd6dc6f32e806e34f485db5b2874f1b6997c1635
Binary files /dev/null and b/PAR 152/cone_dataset/107.jpg differ
diff --git a/PAR 152/cone_dataset/107.xml b/PAR 152/cone_dataset/107.xml
new file mode 100644
index 0000000000000000000000000000000000000000..7df09c28143339caef9663afa46044eacf8aec12
--- /dev/null
+++ b/PAR 152/cone_dataset/107.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>107.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\107.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>493</width>
+		<height>594</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>189</xmin>
+			<ymin>224</ymin>
+			<xmax>316</xmax>
+			<ymax>441</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/108.jpg b/PAR 152/cone_dataset/108.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..d42fdba9fc565cb19b93e68e3b9a7e249b384edd
Binary files /dev/null and b/PAR 152/cone_dataset/108.jpg differ
diff --git a/PAR 152/cone_dataset/108.xml b/PAR 152/cone_dataset/108.xml
new file mode 100644
index 0000000000000000000000000000000000000000..fd46a3df4dea17af6e9cc0341e47b4caa4151dfe
--- /dev/null
+++ b/PAR 152/cone_dataset/108.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>108.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\108.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>395</width>
+		<height>594</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>49</xmin>
+			<ymin>37</ymin>
+			<xmax>353</xmax>
+			<ymax>587</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/109.jpg b/PAR 152/cone_dataset/109.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..2f0d11ebd8ff59aad8619347acf3b1193a388f45
Binary files /dev/null and b/PAR 152/cone_dataset/109.jpg differ
diff --git a/PAR 152/cone_dataset/109.xml b/PAR 152/cone_dataset/109.xml
new file mode 100644
index 0000000000000000000000000000000000000000..7c8b5113f509fd1910898a775309dbd7e37b5a93
--- /dev/null
+++ b/PAR 152/cone_dataset/109.xml	
@@ -0,0 +1,98 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>109.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\109.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>507</width>
+		<height>338</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>298</xmin>
+			<ymin>224</ymin>
+			<xmax>333</xmax>
+			<ymax>273</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>246</xmin>
+			<ymin>225</ymin>
+			<xmax>275</xmax>
+			<ymax>274</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>186</xmin>
+			<ymin>224</ymin>
+			<xmax>219</xmax>
+			<ymax>273</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>134</xmin>
+			<ymin>226</ymin>
+			<xmax>168</xmax>
+			<ymax>269</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>348</xmin>
+			<ymin>226</ymin>
+			<xmax>377</xmax>
+			<ymax>270</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>379</xmin>
+			<ymin>226</ymin>
+			<xmax>407</xmax>
+			<ymax>269</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>99</xmin>
+			<ymin>225</ymin>
+			<xmax>131</xmax>
+			<ymax>267</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/11.jpg b/PAR 152/cone_dataset/11.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..d4587717f7889bca35ef1967bd6a750dcff60a58
Binary files /dev/null and b/PAR 152/cone_dataset/11.jpg differ
diff --git a/PAR 152/cone_dataset/11.xml b/PAR 152/cone_dataset/11.xml
new file mode 100644
index 0000000000000000000000000000000000000000..4f2ab8385285dd4e1750c7607e44353d17e93df8
--- /dev/null
+++ b/PAR 152/cone_dataset/11.xml	
@@ -0,0 +1,134 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>11.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\11.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>508</width>
+		<height>338</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>400</xmin>
+			<ymin>20</ymin>
+			<xmax>508</xmax>
+			<ymax>255</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>294</xmin>
+			<ymin>19</ymin>
+			<xmax>405</xmax>
+			<ymax>205</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>226</xmin>
+			<ymin>18</ymin>
+			<xmax>312</xmax>
+			<ymax>172</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>164</xmin>
+			<ymin>15</ymin>
+			<xmax>243</xmax>
+			<ymax>148</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>107</xmin>
+			<ymin>12</ymin>
+			<xmax>172</xmax>
+			<ymax>125</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>84</xmin>
+			<ymin>13</ymin>
+			<xmax>135</xmax>
+			<ymax>114</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>58</xmin>
+			<ymin>11</ymin>
+			<xmax>99</xmax>
+			<ymax>102</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>310</xmin>
+			<ymin>7</ymin>
+			<xmax>341</xmax>
+			<ymax>64</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>423</xmin>
+			<ymin>8</ymin>
+			<xmax>464</xmax>
+			<ymax>79</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>171</xmin>
+			<ymin>8</ymin>
+			<xmax>194</xmax>
+			<ymax>46</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/110.jpg b/PAR 152/cone_dataset/110.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..4b646a06ec36ecfa82aa3e6f5b1edebe9af803c7
Binary files /dev/null and b/PAR 152/cone_dataset/110.jpg differ
diff --git a/PAR 152/cone_dataset/110.xml b/PAR 152/cone_dataset/110.xml
new file mode 100644
index 0000000000000000000000000000000000000000..ca3674f25b2a0174db035fe033d5cf1191c0bf64
--- /dev/null
+++ b/PAR 152/cone_dataset/110.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>110.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\110.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>413</width>
+		<height>416</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>109</xmin>
+			<ymin>61</ymin>
+			<xmax>269</xmax>
+			<ymax>324</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/111.jpg b/PAR 152/cone_dataset/111.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..a4396847afc3922ec74260655a5147bd036332bc
Binary files /dev/null and b/PAR 152/cone_dataset/111.jpg differ
diff --git a/PAR 152/cone_dataset/111.xml b/PAR 152/cone_dataset/111.xml
new file mode 100644
index 0000000000000000000000000000000000000000..c193e113c287d37b2e3e40ae61d3e357e2c7b69e
--- /dev/null
+++ b/PAR 152/cone_dataset/111.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>111.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\111.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>359</width>
+		<height>478</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>1</xmin>
+			<ymin>18</ymin>
+			<xmax>148</xmax>
+			<ymax>270</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/112.jpg b/PAR 152/cone_dataset/112.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..aea7e0c07f1233fbd8234f66cb8eec86e463b745
Binary files /dev/null and b/PAR 152/cone_dataset/112.jpg differ
diff --git a/PAR 152/cone_dataset/112.xml b/PAR 152/cone_dataset/112.xml
new file mode 100644
index 0000000000000000000000000000000000000000..6b5ae328e4255250e9da0f97637d182e5bdaa2dd
--- /dev/null
+++ b/PAR 152/cone_dataset/112.xml	
@@ -0,0 +1,38 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>112.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\112.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>507</width>
+		<height>337</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>233</xmin>
+			<ymin>186</ymin>
+			<xmax>303</xmax>
+			<ymax>304</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>31</xmin>
+			<ymin>194</ymin>
+			<xmax>108</xmax>
+			<ymax>312</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/113.jpg b/PAR 152/cone_dataset/113.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..8a620706850e254cbba0f18b660ce4cf018d6ecd
Binary files /dev/null and b/PAR 152/cone_dataset/113.jpg differ
diff --git a/PAR 152/cone_dataset/113.xml b/PAR 152/cone_dataset/113.xml
new file mode 100644
index 0000000000000000000000000000000000000000..f215a8926395304615b57553496ff2916527559e
--- /dev/null
+++ b/PAR 152/cone_dataset/113.xml	
@@ -0,0 +1,38 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>113.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\113.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>507</width>
+		<height>337</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>166</xmin>
+			<ymin>210</ymin>
+			<xmax>206</xmax>
+			<ymax>273</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>52</xmin>
+			<ymin>212</ymin>
+			<xmax>95</xmax>
+			<ymax>275</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/114.jpg b/PAR 152/cone_dataset/114.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..7876fa36c5549296476537ae24151b473365dde1
Binary files /dev/null and b/PAR 152/cone_dataset/114.jpg differ
diff --git a/PAR 152/cone_dataset/114.xml b/PAR 152/cone_dataset/114.xml
new file mode 100644
index 0000000000000000000000000000000000000000..63c7af1c30678797a8487a0db6c79f503546bbf2
--- /dev/null
+++ b/PAR 152/cone_dataset/114.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>114.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\114.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>359</width>
+		<height>478</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>113</xmin>
+			<ymin>207</ymin>
+			<xmax>280</xmax>
+			<ymax>461</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/115.jpg b/PAR 152/cone_dataset/115.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..f30c3a3e24cc899948e634351742c76762aef97f
Binary files /dev/null and b/PAR 152/cone_dataset/115.jpg differ
diff --git a/PAR 152/cone_dataset/115.xml b/PAR 152/cone_dataset/115.xml
new file mode 100644
index 0000000000000000000000000000000000000000..bf71ccf9a473f81f5cb5cd4938f212e8ddfbd312
--- /dev/null
+++ b/PAR 152/cone_dataset/115.xml	
@@ -0,0 +1,74 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>115.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\115.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>507</width>
+		<height>338</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>384</xmin>
+			<ymin>235</ymin>
+			<xmax>439</xmax>
+			<ymax>316</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>459</xmin>
+			<ymin>234</ymin>
+			<xmax>507</xmax>
+			<ymax>315</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>172</xmin>
+			<ymin>213</ymin>
+			<xmax>197</xmax>
+			<ymax>262</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>40</xmin>
+			<ymin>194</ymin>
+			<xmax>61</xmax>
+			<ymax>230</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>144</xmin>
+			<ymin>209</ymin>
+			<xmax>173</xmax>
+			<ymax>259</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/116.jpg b/PAR 152/cone_dataset/116.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..0b1a61b2f03768f49e4f1662b6d490d579dfd756
Binary files /dev/null and b/PAR 152/cone_dataset/116.jpg differ
diff --git a/PAR 152/cone_dataset/116.xml b/PAR 152/cone_dataset/116.xml
new file mode 100644
index 0000000000000000000000000000000000000000..ccb5344abb84b37663afcb63de06581f18f280e3
--- /dev/null
+++ b/PAR 152/cone_dataset/116.xml	
@@ -0,0 +1,74 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>116.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\116.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>416</width>
+		<height>412</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>193</xmin>
+			<ymin>216</ymin>
+			<xmax>281</xmax>
+			<ymax>397</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>139</xmin>
+			<ymin>183</ymin>
+			<xmax>217</xmax>
+			<ymax>331</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>245</xmin>
+			<ymin>138</ymin>
+			<xmax>334</xmax>
+			<ymax>182</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>132</xmin>
+			<ymin>117</ymin>
+			<xmax>186</xmax>
+			<ymax>236</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>188</xmin>
+			<ymin>114</ymin>
+			<xmax>241</xmax>
+			<ymax>210</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/117.jpg b/PAR 152/cone_dataset/117.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..929b1e3835c003981a2c85723d6c10655584dc11
Binary files /dev/null and b/PAR 152/cone_dataset/117.jpg differ
diff --git a/PAR 152/cone_dataset/117.xml b/PAR 152/cone_dataset/117.xml
new file mode 100644
index 0000000000000000000000000000000000000000..2f72f24c9191dcead939676baa69cd3f7f328c9b
--- /dev/null
+++ b/PAR 152/cone_dataset/117.xml	
@@ -0,0 +1,74 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>117.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\117.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>478</width>
+		<height>359</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>156</xmin>
+			<ymin>83</ymin>
+			<xmax>255</xmax>
+			<ymax>278</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>82</xmin>
+			<ymin>36</ymin>
+			<xmax>170</xmax>
+			<ymax>206</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>256</xmin>
+			<ymin>74</ymin>
+			<xmax>359</xmax>
+			<ymax>251</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>158</xmin>
+			<ymin>14</ymin>
+			<xmax>241</xmax>
+			<ymax>160</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>246</xmin>
+			<ymin>28</ymin>
+			<xmax>338</xmax>
+			<ymax>175</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/118.jpg b/PAR 152/cone_dataset/118.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..af3dfcff6cdb9b7e9583209cea8216e4938f65ed
Binary files /dev/null and b/PAR 152/cone_dataset/118.jpg differ
diff --git a/PAR 152/cone_dataset/118.xml b/PAR 152/cone_dataset/118.xml
new file mode 100644
index 0000000000000000000000000000000000000000..68e0cefa006ddf1881ea87bedb7a289fe153eeb3
--- /dev/null
+++ b/PAR 152/cone_dataset/118.xml	
@@ -0,0 +1,74 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>118.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\118.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>1024</width>
+		<height>665</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>671</xmin>
+			<ymin>439</ymin>
+			<xmax>773</xmax>
+			<ymax>587</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>537</xmin>
+			<ymin>440</ymin>
+			<xmax>653</xmax>
+			<ymax>588</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>346</xmin>
+			<ymin>442</ymin>
+			<xmax>457</xmax>
+			<ymax>572</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>95</xmin>
+			<ymin>445</ymin>
+			<xmax>214</xmax>
+			<ymax>565</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>1</xmin>
+			<ymin>442</ymin>
+			<xmax>59</xmax>
+			<ymax>553</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/119.jpg b/PAR 152/cone_dataset/119.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..a0495e3be0bd4ff0e0f07906c3a099da8797fed2
Binary files /dev/null and b/PAR 152/cone_dataset/119.jpg differ
diff --git a/PAR 152/cone_dataset/119.xml b/PAR 152/cone_dataset/119.xml
new file mode 100644
index 0000000000000000000000000000000000000000..4f383288670b2a8db0cd41847b974c8dfc09fc78
--- /dev/null
+++ b/PAR 152/cone_dataset/119.xml	
@@ -0,0 +1,38 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>119.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\119.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>509</width>
+		<height>339</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>73</xmin>
+			<ymin>197</ymin>
+			<xmax>127</xmax>
+			<ymax>303</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>101</xmin>
+			<ymin>188</ymin>
+			<xmax>151</xmax>
+			<ymax>276</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/12.jpg b/PAR 152/cone_dataset/12.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..82412f5b9cf30baed42b8bf5095aa5e5c3f15381
Binary files /dev/null and b/PAR 152/cone_dataset/12.jpg differ
diff --git a/PAR 152/cone_dataset/12.xml b/PAR 152/cone_dataset/12.xml
new file mode 100644
index 0000000000000000000000000000000000000000..9f58bcc29b2a73e0c6219b1587ee49c1d6b9bb4c
--- /dev/null
+++ b/PAR 152/cone_dataset/12.xml	
@@ -0,0 +1,50 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>12.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\12.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>359</width>
+		<height>479</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>121</xmin>
+			<ymin>115</ymin>
+			<xmax>340</xmax>
+			<ymax>426</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>104</xmin>
+			<ymin>175</ymin>
+			<xmax>192</xmax>
+			<ymax>314</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>74</xmin>
+			<ymin>196</ymin>
+			<xmax>118</xmax>
+			<ymax>289</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/120.jpg b/PAR 152/cone_dataset/120.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..dbd1b526509aadc7ed5f947cc5ebe6f084aedc1b
Binary files /dev/null and b/PAR 152/cone_dataset/120.jpg differ
diff --git a/PAR 152/cone_dataset/120.xml b/PAR 152/cone_dataset/120.xml
new file mode 100644
index 0000000000000000000000000000000000000000..1c0cab18d7ae5a65a8e117f40bedd15886b4bf49
--- /dev/null
+++ b/PAR 152/cone_dataset/120.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>120.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\120.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>506</width>
+		<height>337</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>264</xmin>
+			<ymin>213</ymin>
+			<xmax>314</xmax>
+			<ymax>308</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/121.jpg b/PAR 152/cone_dataset/121.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..865719aaa22dada261f0bcd2258246e9710535e3
Binary files /dev/null and b/PAR 152/cone_dataset/121.jpg differ
diff --git a/PAR 152/cone_dataset/121.xml b/PAR 152/cone_dataset/121.xml
new file mode 100644
index 0000000000000000000000000000000000000000..944761234557b7ea96673f2349f6a8b12108ca93
--- /dev/null
+++ b/PAR 152/cone_dataset/121.xml	
@@ -0,0 +1,86 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>121.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\121.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>507</width>
+		<height>337</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>84</xmin>
+			<ymin>150</ymin>
+			<xmax>175</xmax>
+			<ymax>337</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>214</xmin>
+			<ymin>117</ymin>
+			<xmax>233</xmax>
+			<ymax>153</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>281</xmin>
+			<ymin>121</ymin>
+			<xmax>314</xmax>
+			<ymax>174</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>259</xmin>
+			<ymin>117</ymin>
+			<xmax>283</xmax>
+			<ymax>159</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>235</xmin>
+			<ymin>118</ymin>
+			<xmax>252</xmax>
+			<ymax>151</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>186</xmin>
+			<ymin>117</ymin>
+			<xmax>199</xmax>
+			<ymax>137</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/122.jpg b/PAR 152/cone_dataset/122.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..89e170917e0d48164f90581bd1a9aabf691af34e
Binary files /dev/null and b/PAR 152/cone_dataset/122.jpg differ
diff --git a/PAR 152/cone_dataset/122.xml b/PAR 152/cone_dataset/122.xml
new file mode 100644
index 0000000000000000000000000000000000000000..b9d947a59c2202afa2f9fb04dd505affde1fb881
--- /dev/null
+++ b/PAR 152/cone_dataset/122.xml	
@@ -0,0 +1,38 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>122.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\122.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>339</width>
+		<height>510</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>145</xmin>
+			<ymin>377</ymin>
+			<xmax>186</xmax>
+			<ymax>445</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>251</xmin>
+			<ymin>357</ymin>
+			<xmax>263</xmax>
+			<ymax>392</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/123.jpg b/PAR 152/cone_dataset/123.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..08bc6f4fc4af17feb909db1b6dd7f2e093d7bdcf
Binary files /dev/null and b/PAR 152/cone_dataset/123.jpg differ
diff --git a/PAR 152/cone_dataset/123.xml b/PAR 152/cone_dataset/123.xml
new file mode 100644
index 0000000000000000000000000000000000000000..0eb8bd7ef2d788a9d84a48dac0cb01ec3e2a1c28
--- /dev/null
+++ b/PAR 152/cone_dataset/123.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>123.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\123.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>508</width>
+		<height>337</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>51</xmin>
+			<ymin>44</ymin>
+			<xmax>123</xmax>
+			<ymax>240</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/124.jpg b/PAR 152/cone_dataset/124.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..1b3ce2f1217c628809e00db20e5bce24983091e6
Binary files /dev/null and b/PAR 152/cone_dataset/124.jpg differ
diff --git a/PAR 152/cone_dataset/124.xml b/PAR 152/cone_dataset/124.xml
new file mode 100644
index 0000000000000000000000000000000000000000..b5fe822327a216ca72025717584bca76c40d97b1
--- /dev/null
+++ b/PAR 152/cone_dataset/124.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>124.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\124.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>359</width>
+		<height>479</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>125</xmin>
+			<ymin>259</ymin>
+			<xmax>225</xmax>
+			<ymax>397</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/125.jpg b/PAR 152/cone_dataset/125.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..a361e399665eb15e12d8b0f6ffebcfa8e78836cd
Binary files /dev/null and b/PAR 152/cone_dataset/125.jpg differ
diff --git a/PAR 152/cone_dataset/125.xml b/PAR 152/cone_dataset/125.xml
new file mode 100644
index 0000000000000000000000000000000000000000..245b581fc7f24b23d4a358f830bea9315307d9fc
--- /dev/null
+++ b/PAR 152/cone_dataset/125.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>125.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\125.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>507</width>
+		<height>338</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>195</xmin>
+			<ymin>93</ymin>
+			<xmax>309</xmax>
+			<ymax>264</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/126.jpg b/PAR 152/cone_dataset/126.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..ae566fa972545bad4b0cafcf8b6eb478c96a63fe
Binary files /dev/null and b/PAR 152/cone_dataset/126.jpg differ
diff --git a/PAR 152/cone_dataset/126.xml b/PAR 152/cone_dataset/126.xml
new file mode 100644
index 0000000000000000000000000000000000000000..1d15e09d77114e104f70ae17f1e1ccabdc3f98d1
--- /dev/null
+++ b/PAR 152/cone_dataset/126.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>126.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\126.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>338</width>
+		<height>507</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>12</xmin>
+			<ymin>31</ymin>
+			<xmax>317</xmax>
+			<ymax>477</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/127.jpg b/PAR 152/cone_dataset/127.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..3c384b8ef14a07741777d8467d0f3f01ea296d92
Binary files /dev/null and b/PAR 152/cone_dataset/127.jpg differ
diff --git a/PAR 152/cone_dataset/127.xml b/PAR 152/cone_dataset/127.xml
new file mode 100644
index 0000000000000000000000000000000000000000..53f02dd33dc696122921082e84b4b2bc94b896b5
--- /dev/null
+++ b/PAR 152/cone_dataset/127.xml	
@@ -0,0 +1,122 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>127.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\127.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>529</width>
+		<height>327</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>50</xmin>
+			<ymin>154</ymin>
+			<xmax>154</xmax>
+			<ymax>320</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>147</xmin>
+			<ymin>125</ymin>
+			<xmax>241</xmax>
+			<ymax>279</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>225</xmin>
+			<ymin>104</ymin>
+			<xmax>312</xmax>
+			<ymax>250</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>286</xmin>
+			<ymin>87</ymin>
+			<xmax>367</xmax>
+			<ymax>226</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>339</xmin>
+			<ymin>73</ymin>
+			<xmax>408</xmax>
+			<ymax>202</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>379</xmin>
+			<ymin>59</ymin>
+			<xmax>445</xmax>
+			<ymax>185</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>414</xmin>
+			<ymin>46</ymin>
+			<xmax>478</xmax>
+			<ymax>163</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>459</xmin>
+			<ymin>31</ymin>
+			<xmax>519</xmax>
+			<ymax>145</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>497</xmin>
+			<ymin>21</ymin>
+			<xmax>529</xmax>
+			<ymax>127</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/128.jpg b/PAR 152/cone_dataset/128.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..9f8ee6d4c11cb00f8801d6fecf48e58e470e7c86
Binary files /dev/null and b/PAR 152/cone_dataset/128.jpg differ
diff --git a/PAR 152/cone_dataset/128.xml b/PAR 152/cone_dataset/128.xml
new file mode 100644
index 0000000000000000000000000000000000000000..fab7b12143c8c8b859dd49a6354cee9bab868c80
--- /dev/null
+++ b/PAR 152/cone_dataset/128.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>128.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\128.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>339</width>
+		<height>506</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>69</xmin>
+			<ymin>52</ymin>
+			<xmax>296</xmax>
+			<ymax>462</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/129.jpg b/PAR 152/cone_dataset/129.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..0d6c00d96e51c81f7ee267f53b9eb89720532d1f
Binary files /dev/null and b/PAR 152/cone_dataset/129.jpg differ
diff --git a/PAR 152/cone_dataset/129.xml b/PAR 152/cone_dataset/129.xml
new file mode 100644
index 0000000000000000000000000000000000000000..ad637f0742b7c7f9f8b3d9d7b89c664781a70572
--- /dev/null
+++ b/PAR 152/cone_dataset/129.xml	
@@ -0,0 +1,74 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>129.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\129.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>509</width>
+		<height>339</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>314</xmin>
+			<ymin>202</ymin>
+			<xmax>386</xmax>
+			<ymax>318</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>93</xmin>
+			<ymin>207</ymin>
+			<xmax>158</xmax>
+			<ymax>313</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>403</xmin>
+			<ymin>217</ymin>
+			<xmax>446</xmax>
+			<ymax>285</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>276</xmin>
+			<ymin>215</ymin>
+			<xmax>322</xmax>
+			<ymax>291</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>17</xmin>
+			<ymin>223</ymin>
+			<xmax>47</xmax>
+			<ymax>273</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/13.jpg b/PAR 152/cone_dataset/13.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..dd1bb05d815945a7fffeadabdcf251cf41b577ff
Binary files /dev/null and b/PAR 152/cone_dataset/13.jpg differ
diff --git a/PAR 152/cone_dataset/13.xml b/PAR 152/cone_dataset/13.xml
new file mode 100644
index 0000000000000000000000000000000000000000..fab6df7a28fc5d5b6c3a24e81fe756167fa26c62
--- /dev/null
+++ b/PAR 152/cone_dataset/13.xml	
@@ -0,0 +1,62 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>13.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\13.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>414</width>
+		<height>414</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>233</xmin>
+			<ymin>132</ymin>
+			<xmax>374</xmax>
+			<ymax>368</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>154</xmin>
+			<ymin>95</ymin>
+			<xmax>258</xmax>
+			<ymax>270</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>89</xmin>
+			<ymin>79</ymin>
+			<xmax>176</xmax>
+			<ymax>219</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>33</xmin>
+			<ymin>65</ymin>
+			<xmax>108</xmax>
+			<ymax>183</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/130.jpg b/PAR 152/cone_dataset/130.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..479508971ee955463799ad75b5feeb321b66e63b
Binary files /dev/null and b/PAR 152/cone_dataset/130.jpg differ
diff --git a/PAR 152/cone_dataset/130.xml b/PAR 152/cone_dataset/130.xml
new file mode 100644
index 0000000000000000000000000000000000000000..9949338f42a422895792c5c8759efe6b463f8c8a
--- /dev/null
+++ b/PAR 152/cone_dataset/130.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>130.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\130.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>396</width>
+		<height>430</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>143</xmin>
+			<ymin>48</ymin>
+			<xmax>305</xmax>
+			<ymax>280</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/131.jpg b/PAR 152/cone_dataset/131.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..64d169561db4552fbd7ce3af3e8c89ea91a249c0
Binary files /dev/null and b/PAR 152/cone_dataset/131.jpg differ
diff --git a/PAR 152/cone_dataset/131.xml b/PAR 152/cone_dataset/131.xml
new file mode 100644
index 0000000000000000000000000000000000000000..57720ed77f73671baeeda38413d6433a2e4a450c
--- /dev/null
+++ b/PAR 152/cone_dataset/131.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>131.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\131.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>375</width>
+		<height>458</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>71</xmin>
+			<ymin>40</ymin>
+			<xmax>296</xmax>
+			<ymax>407</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/132.jpg b/PAR 152/cone_dataset/132.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..91f37aedcc3629db74dae46d6144df4f3e54537e
Binary files /dev/null and b/PAR 152/cone_dataset/132.jpg differ
diff --git a/PAR 152/cone_dataset/132.xml b/PAR 152/cone_dataset/132.xml
new file mode 100644
index 0000000000000000000000000000000000000000..94d0d12a648cdf7994145766520f81a43f8f7d9b
--- /dev/null
+++ b/PAR 152/cone_dataset/132.xml	
@@ -0,0 +1,38 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>132.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\132.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>507</width>
+		<height>338</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>60</xmin>
+			<ymin>63</ymin>
+			<xmax>198</xmax>
+			<ymax>300</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>177</xmin>
+			<ymin>139</ymin>
+			<xmax>380</xmax>
+			<ymax>300</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/133.jpg b/PAR 152/cone_dataset/133.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..3e20f026c2ce8d90c06d48db7e4e1d616e3e2da4
Binary files /dev/null and b/PAR 152/cone_dataset/133.jpg differ
diff --git a/PAR 152/cone_dataset/133.xml b/PAR 152/cone_dataset/133.xml
new file mode 100644
index 0000000000000000000000000000000000000000..286952000d06ca9843d44ac6927b25023ccbbd99
--- /dev/null
+++ b/PAR 152/cone_dataset/133.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>133.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\133.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>414</width>
+		<height>414</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>65</xmin>
+			<ymin>9</ymin>
+			<xmax>343</xmax>
+			<ymax>399</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/134.jpg b/PAR 152/cone_dataset/134.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..f88e78bd50d67eacdae68a139e2ebc590614a378
Binary files /dev/null and b/PAR 152/cone_dataset/134.jpg differ
diff --git a/PAR 152/cone_dataset/134.xml b/PAR 152/cone_dataset/134.xml
new file mode 100644
index 0000000000000000000000000000000000000000..6e85e037a6cbddd243198021b6c5613493aba001
--- /dev/null
+++ b/PAR 152/cone_dataset/134.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>134.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\134.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>414</width>
+		<height>414</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>87</xmin>
+			<ymin>6</ymin>
+			<xmax>331</xmax>
+			<ymax>409</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/135.jpg b/PAR 152/cone_dataset/135.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..377db54a5ff54c1a336f0d681539cf3b72d3e477
Binary files /dev/null and b/PAR 152/cone_dataset/135.jpg differ
diff --git a/PAR 152/cone_dataset/135.xml b/PAR 152/cone_dataset/135.xml
new file mode 100644
index 0000000000000000000000000000000000000000..47ff1be42fc3e7096f279a7e5b6c0be952c624b1
--- /dev/null
+++ b/PAR 152/cone_dataset/135.xml	
@@ -0,0 +1,50 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>135.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\135.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>478</width>
+		<height>359</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>142</xmin>
+			<ymin>133</ymin>
+			<xmax>285</xmax>
+			<ymax>303</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>323</xmin>
+			<ymin>84</ymin>
+			<xmax>443</xmax>
+			<ymax>213</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>40</xmin>
+			<ymin>111</ymin>
+			<xmax>171</xmax>
+			<ymax>245</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/136.jpg b/PAR 152/cone_dataset/136.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..9450c66428b75490742f64aa49e9336e647c0cd2
Binary files /dev/null and b/PAR 152/cone_dataset/136.jpg differ
diff --git a/PAR 152/cone_dataset/136.xml b/PAR 152/cone_dataset/136.xml
new file mode 100644
index 0000000000000000000000000000000000000000..deea19282e6f408d6d097797be297c58181f5578
--- /dev/null
+++ b/PAR 152/cone_dataset/136.xml	
@@ -0,0 +1,110 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>136.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\136.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>837</width>
+		<height>1024</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>485</xmin>
+			<ymin>480</ymin>
+			<xmax>568</xmax>
+			<ymax>621</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>570</xmin>
+			<ymin>412</ymin>
+			<xmax>639</xmax>
+			<ymax>528</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>644</xmin>
+			<ymin>361</ymin>
+			<xmax>704</xmax>
+			<ymax>451</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>714</xmin>
+			<ymin>308</ymin>
+			<xmax>764</xmax>
+			<ymax>385</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>773</xmin>
+			<ymin>273</ymin>
+			<xmax>817</xmax>
+			<ymax>334</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>264</xmin>
+			<ymin>615</ymin>
+			<xmax>390</xmax>
+			<ymax>848</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>126</xmin>
+			<ymin>707</ymin>
+			<xmax>284</xmax>
+			<ymax>985</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>391</xmin>
+			<ymin>642</ymin>
+			<xmax>536</xmax>
+			<ymax>766</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/137.jpg b/PAR 152/cone_dataset/137.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..5ea3555212980a9a5e49999f6e94c863ba876f05
Binary files /dev/null and b/PAR 152/cone_dataset/137.jpg differ
diff --git a/PAR 152/cone_dataset/137.xml b/PAR 152/cone_dataset/137.xml
new file mode 100644
index 0000000000000000000000000000000000000000..5bc04c6a295eeb36113a32e575defe7db9ea83dc
--- /dev/null
+++ b/PAR 152/cone_dataset/137.xml	
@@ -0,0 +1,50 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>137.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\137.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>507</width>
+		<height>338</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>117</xmin>
+			<ymin>168</ymin>
+			<xmax>303</xmax>
+			<ymax>293</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>349</xmin>
+			<ymin>90</ymin>
+			<xmax>492</xmax>
+			<ymax>297</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>283</xmin>
+			<ymin>100</ymin>
+			<xmax>365</xmax>
+			<ymax>259</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/138.jpg b/PAR 152/cone_dataset/138.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..5387d06aecc6a705827ea8a41f516adc2a8c0f82
Binary files /dev/null and b/PAR 152/cone_dataset/138.jpg differ
diff --git a/PAR 152/cone_dataset/138.xml b/PAR 152/cone_dataset/138.xml
new file mode 100644
index 0000000000000000000000000000000000000000..161be36dfd6b0e9ec41456dd28229e5ae5a9aa3f
--- /dev/null
+++ b/PAR 152/cone_dataset/138.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>138.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\138.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>381</width>
+		<height>454</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>6</xmin>
+			<ymin>20</ymin>
+			<xmax>367</xmax>
+			<ymax>434</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/139.jpg b/PAR 152/cone_dataset/139.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..e229ee1c6fb5049866625ded90073c26df5fbecf
Binary files /dev/null and b/PAR 152/cone_dataset/139.jpg differ
diff --git a/PAR 152/cone_dataset/139.xml b/PAR 152/cone_dataset/139.xml
new file mode 100644
index 0000000000000000000000000000000000000000..3a9b7b1544a11328c9dfb89ea523ad1c8c7cf6de
--- /dev/null
+++ b/PAR 152/cone_dataset/139.xml	
@@ -0,0 +1,62 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>139.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\139.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>340</width>
+		<height>508</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>17</xmin>
+			<ymin>264</ymin>
+			<xmax>120</xmax>
+			<ymax>460</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>4</xmin>
+			<ymin>251</ymin>
+			<xmax>55</xmax>
+			<ymax>385</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>208</xmin>
+			<ymin>262</ymin>
+			<xmax>314</xmax>
+			<ymax>460</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>269</xmin>
+			<ymin>244</ymin>
+			<xmax>338</xmax>
+			<ymax>379</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/14.jpg b/PAR 152/cone_dataset/14.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..82412f5b9cf30baed42b8bf5095aa5e5c3f15381
Binary files /dev/null and b/PAR 152/cone_dataset/14.jpg differ
diff --git a/PAR 152/cone_dataset/14.xml b/PAR 152/cone_dataset/14.xml
new file mode 100644
index 0000000000000000000000000000000000000000..ba95eccc852d8f09b42e811e6cfa8410276402e8
--- /dev/null
+++ b/PAR 152/cone_dataset/14.xml	
@@ -0,0 +1,50 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>14.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\14.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>359</width>
+		<height>479</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>118</xmin>
+			<ymin>113</ymin>
+			<xmax>338</xmax>
+			<ymax>423</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>95</xmin>
+			<ymin>175</ymin>
+			<xmax>190</xmax>
+			<ymax>313</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>78</xmin>
+			<ymin>196</ymin>
+			<xmax>120</xmax>
+			<ymax>283</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/140.jpg b/PAR 152/cone_dataset/140.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..24c8addfdc85c4cdcad71731689bcdfe00942d12
Binary files /dev/null and b/PAR 152/cone_dataset/140.jpg differ
diff --git a/PAR 152/cone_dataset/140.xml b/PAR 152/cone_dataset/140.xml
new file mode 100644
index 0000000000000000000000000000000000000000..80e5d6637db504bc0d76836d7136b1569ee4ac43
--- /dev/null
+++ b/PAR 152/cone_dataset/140.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>140.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\140.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>337</width>
+		<height>506</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>183</xmin>
+			<ymin>166</ymin>
+			<xmax>314</xmax>
+			<ymax>388</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/141.jpg b/PAR 152/cone_dataset/141.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..5de0b10d1627f61e7528d6b5325e3e4733e7b497
Binary files /dev/null and b/PAR 152/cone_dataset/141.jpg differ
diff --git a/PAR 152/cone_dataset/141.xml b/PAR 152/cone_dataset/141.xml
new file mode 100644
index 0000000000000000000000000000000000000000..a5a1a653ae50d0e4f05c31bd97a2d3d6a43228ed
--- /dev/null
+++ b/PAR 152/cone_dataset/141.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>141.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\141.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>337</width>
+		<height>508</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>49</xmin>
+			<ymin>45</ymin>
+			<xmax>260</xmax>
+			<ymax>459</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/142.jpg b/PAR 152/cone_dataset/142.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..735e21e65353c8cd3b610714239739b08441ccca
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diff --git a/PAR 152/cone_dataset/142.xml b/PAR 152/cone_dataset/142.xml
new file mode 100644
index 0000000000000000000000000000000000000000..362b7d5e41b8420a34ddfd59cd4f31fb6a4a67f7
--- /dev/null
+++ b/PAR 152/cone_dataset/142.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>142.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\142.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>337</width>
+		<height>506</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>145</xmin>
+			<ymin>291</ymin>
+			<xmax>192</xmax>
+			<ymax>369</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/143.jpg b/PAR 152/cone_dataset/143.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..7ca6552e5e7c57a6c699da7236addae19293c80a
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diff --git a/PAR 152/cone_dataset/143.xml b/PAR 152/cone_dataset/143.xml
new file mode 100644
index 0000000000000000000000000000000000000000..cad7584590ab5dc9fb94d7a6a2496ca9f5745b95
--- /dev/null
+++ b/PAR 152/cone_dataset/143.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>143.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\143.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>338</width>
+		<height>507</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>130</xmin>
+			<ymin>155</ymin>
+			<xmax>304</xmax>
+			<ymax>477</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/144.jpg b/PAR 152/cone_dataset/144.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..4a94d2310e30f9a89e57c534aec31585b0e581c1
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diff --git a/PAR 152/cone_dataset/144.xml b/PAR 152/cone_dataset/144.xml
new file mode 100644
index 0000000000000000000000000000000000000000..457d404573f9d702d11de873691e974b98440eae
--- /dev/null
+++ b/PAR 152/cone_dataset/144.xml	
@@ -0,0 +1,62 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>144.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\144.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>416</width>
+		<height>416</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>158</xmin>
+			<ymin>178</ymin>
+			<xmax>203</xmax>
+			<ymax>247</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>165</xmin>
+			<ymin>339</ymin>
+			<xmax>230</xmax>
+			<ymax>416</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>158</xmin>
+			<ymin>154</ymin>
+			<xmax>184</xmax>
+			<ymax>196</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>186</xmin>
+			<ymin>139</ymin>
+			<xmax>196</xmax>
+			<ymax>153</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/145.jpg b/PAR 152/cone_dataset/145.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..02fb790b1876b308d8e7f3d1ea94f2868543a1be
Binary files /dev/null and b/PAR 152/cone_dataset/145.jpg differ
diff --git a/PAR 152/cone_dataset/145.xml b/PAR 152/cone_dataset/145.xml
new file mode 100644
index 0000000000000000000000000000000000000000..134a8ba3917ac98619e1e797b1a2cee2ff22b332
--- /dev/null
+++ b/PAR 152/cone_dataset/145.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>145.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\145.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>506</width>
+		<height>341</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>273</xmin>
+			<ymin>45</ymin>
+			<xmax>442</xmax>
+			<ymax>337</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/146.jpg b/PAR 152/cone_dataset/146.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..78159f2e458b771b5beb4869ffbedc81385533db
Binary files /dev/null and b/PAR 152/cone_dataset/146.jpg differ
diff --git a/PAR 152/cone_dataset/146.xml b/PAR 152/cone_dataset/146.xml
new file mode 100644
index 0000000000000000000000000000000000000000..74b85a02752cbdb8feec841ecf1019d5f1e19910
--- /dev/null
+++ b/PAR 152/cone_dataset/146.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>146.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\146.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>336</width>
+		<height>509</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>80</xmin>
+			<ymin>119</ymin>
+			<xmax>336</xmax>
+			<ymax>509</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/147.jpg b/PAR 152/cone_dataset/147.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..7e10982a0c453f90bb81366ff7aa5e1397e2644c
Binary files /dev/null and b/PAR 152/cone_dataset/147.jpg differ
diff --git a/PAR 152/cone_dataset/147.xml b/PAR 152/cone_dataset/147.xml
new file mode 100644
index 0000000000000000000000000000000000000000..482aa8a09094d5941bb6a34274f4fb355a08528b
--- /dev/null
+++ b/PAR 152/cone_dataset/147.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>147.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\147.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>347</width>
+		<height>491</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>40</xmin>
+			<ymin>126</ymin>
+			<xmax>240</xmax>
+			<ymax>450</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/148.jpg b/PAR 152/cone_dataset/148.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..48cbd9952e14d2b7c7b69e9cf78ec4e1ff8d4a1f
Binary files /dev/null and b/PAR 152/cone_dataset/148.jpg differ
diff --git a/PAR 152/cone_dataset/148.xml b/PAR 152/cone_dataset/148.xml
new file mode 100644
index 0000000000000000000000000000000000000000..bd5eab860bb705b27b17467cb17edaec9c9abcfe
--- /dev/null
+++ b/PAR 152/cone_dataset/148.xml	
@@ -0,0 +1,38 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>148.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\148.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>507</width>
+		<height>337</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>10</xmin>
+			<ymin>16</ymin>
+			<xmax>175</xmax>
+			<ymax>327</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>339</xmin>
+			<ymin>18</ymin>
+			<xmax>507</xmax>
+			<ymax>328</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/149.jpg b/PAR 152/cone_dataset/149.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..3314ed2b67ded8ec1ea2b8ed287e4bac72e75186
Binary files /dev/null and b/PAR 152/cone_dataset/149.jpg differ
diff --git a/PAR 152/cone_dataset/149.xml b/PAR 152/cone_dataset/149.xml
new file mode 100644
index 0000000000000000000000000000000000000000..cca92371bac194de8c02bf0f9096e1d625a62a31
--- /dev/null
+++ b/PAR 152/cone_dataset/149.xml	
@@ -0,0 +1,50 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>149.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\149.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>509</width>
+		<height>339</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>10</xmin>
+			<ymin>100</ymin>
+			<xmax>94</xmax>
+			<ymax>216</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>131</xmin>
+			<ymin>104</ymin>
+			<xmax>221</xmax>
+			<ymax>214</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>483</xmin>
+			<ymin>140</ymin>
+			<xmax>509</xmax>
+			<ymax>212</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/15.jpg b/PAR 152/cone_dataset/15.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..90ef67dca44063a2eaada849f5ecb271bf67f229
Binary files /dev/null and b/PAR 152/cone_dataset/15.jpg differ
diff --git a/PAR 152/cone_dataset/15.xml b/PAR 152/cone_dataset/15.xml
new file mode 100644
index 0000000000000000000000000000000000000000..019d4deb362b19a63e57c1c0f8449949b51aea10
--- /dev/null
+++ b/PAR 152/cone_dataset/15.xml	
@@ -0,0 +1,38 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>15.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\15.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>478</width>
+		<height>359</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>186</xmin>
+			<ymin>49</ymin>
+			<xmax>357</xmax>
+			<ymax>287</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>381</xmin>
+			<ymin>166</ymin>
+			<xmax>449</xmax>
+			<ymax>263</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/150.jpg b/PAR 152/cone_dataset/150.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..93b977b1f8b516f4df2a60653e0324ae17ed0788
Binary files /dev/null and b/PAR 152/cone_dataset/150.jpg differ
diff --git a/PAR 152/cone_dataset/150.xml b/PAR 152/cone_dataset/150.xml
new file mode 100644
index 0000000000000000000000000000000000000000..2995b80b8d861da6272db85aa08ee34553c693f5
--- /dev/null
+++ b/PAR 152/cone_dataset/150.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>150.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\150.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>359</width>
+		<height>479</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>112</xmin>
+			<ymin>97</ymin>
+			<xmax>244</xmax>
+			<ymax>345</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/151.jpg b/PAR 152/cone_dataset/151.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..d4c00af736330984eaf97ef3f4f868e2e4372562
Binary files /dev/null and b/PAR 152/cone_dataset/151.jpg differ
diff --git a/PAR 152/cone_dataset/151.xml b/PAR 152/cone_dataset/151.xml
new file mode 100644
index 0000000000000000000000000000000000000000..2034df9012fc38b48bb7dfa3f2bb51240c62716b
--- /dev/null
+++ b/PAR 152/cone_dataset/151.xml	
@@ -0,0 +1,62 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>151.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\151.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>479</width>
+		<height>359</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>190</xmin>
+			<ymin>89</ymin>
+			<xmax>270</xmax>
+			<ymax>217</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>236</xmin>
+			<ymin>36</ymin>
+			<xmax>289</xmax>
+			<ymax>123</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>73</xmin>
+			<ymin>244</ymin>
+			<xmax>146</xmax>
+			<ymax>359</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>249</xmin>
+			<ymin>11</ymin>
+			<xmax>291</xmax>
+			<ymax>77</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/152.jpg b/PAR 152/cone_dataset/152.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..06c9fd5df48060c8b4fa6f32683295f5db1eab24
Binary files /dev/null and b/PAR 152/cone_dataset/152.jpg differ
diff --git a/PAR 152/cone_dataset/152.xml b/PAR 152/cone_dataset/152.xml
new file mode 100644
index 0000000000000000000000000000000000000000..d0171f2627166e579829b798383327e9d0931963
--- /dev/null
+++ b/PAR 152/cone_dataset/152.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>152.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\152.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>509</width>
+		<height>339</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>274</xmin>
+			<ymin>57</ymin>
+			<xmax>391</xmax>
+			<ymax>196</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/153.jpg b/PAR 152/cone_dataset/153.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..d54a287026558bd6139804e35315c442c1bbf3bf
Binary files /dev/null and b/PAR 152/cone_dataset/153.jpg differ
diff --git a/PAR 152/cone_dataset/153.xml b/PAR 152/cone_dataset/153.xml
new file mode 100644
index 0000000000000000000000000000000000000000..396945bb8fd7f1780c6c73acb7155eaa7710ac4e
--- /dev/null
+++ b/PAR 152/cone_dataset/153.xml	
@@ -0,0 +1,74 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>153.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\153.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>509</width>
+		<height>339</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>91</xmin>
+			<ymin>172</ymin>
+			<xmax>172</xmax>
+			<ymax>325</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>319</xmin>
+			<ymin>174</ymin>
+			<xmax>422</xmax>
+			<ymax>333</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>422</xmin>
+			<ymin>156</ymin>
+			<xmax>452</xmax>
+			<ymax>207</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>339</xmin>
+			<ymin>152</ymin>
+			<xmax>374</xmax>
+			<ymax>207</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>487</xmin>
+			<ymin>156</ymin>
+			<xmax>509</xmax>
+			<ymax>204</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/154.jpg b/PAR 152/cone_dataset/154.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..ad6353f5b23ce13a29e1cf876bf9a745408fde78
Binary files /dev/null and b/PAR 152/cone_dataset/154.jpg differ
diff --git a/PAR 152/cone_dataset/154.xml b/PAR 152/cone_dataset/154.xml
new file mode 100644
index 0000000000000000000000000000000000000000..2756ff8e2a1174a065188a6337e012b39a2d7889
--- /dev/null
+++ b/PAR 152/cone_dataset/154.xml	
@@ -0,0 +1,50 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>154.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\154.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>359</width>
+		<height>479</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>110</xmin>
+			<ymin>68</ymin>
+			<xmax>178</xmax>
+			<ymax>179</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>217</xmin>
+			<ymin>180</ymin>
+			<xmax>340</xmax>
+			<ymax>299</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>135</xmin>
+			<ymin>143</ymin>
+			<xmax>224</xmax>
+			<ymax>244</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/155.jpg b/PAR 152/cone_dataset/155.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..aef3841892159501ee808847ea22c4e2ad7e09c9
Binary files /dev/null and b/PAR 152/cone_dataset/155.jpg differ
diff --git a/PAR 152/cone_dataset/155.xml b/PAR 152/cone_dataset/155.xml
new file mode 100644
index 0000000000000000000000000000000000000000..82bc0d357a80d7177d2a19235eddea2f11f8b47b
--- /dev/null
+++ b/PAR 152/cone_dataset/155.xml	
@@ -0,0 +1,62 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>155.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\155.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>479</width>
+		<height>359</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>120</xmin>
+			<ymin>67</ymin>
+			<xmax>222</xmax>
+			<ymax>253</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>1</xmin>
+			<ymin>59</ymin>
+			<xmax>102</xmax>
+			<ymax>256</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>296</xmin>
+			<ymin>77</ymin>
+			<xmax>400</xmax>
+			<ymax>253</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>392</xmin>
+			<ymin>74</ymin>
+			<xmax>479</xmax>
+			<ymax>260</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/156.jpg b/PAR 152/cone_dataset/156.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..4d66846e14441b98c2bdc9786b527335a3493e56
Binary files /dev/null and b/PAR 152/cone_dataset/156.jpg differ
diff --git a/PAR 152/cone_dataset/156.xml b/PAR 152/cone_dataset/156.xml
new file mode 100644
index 0000000000000000000000000000000000000000..80b4b0b170dcf0594ab1c5fb073c9121da2887fc
--- /dev/null
+++ b/PAR 152/cone_dataset/156.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>156.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\156.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>508</width>
+		<height>340</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>207</xmin>
+			<ymin>93</ymin>
+			<xmax>315</xmax>
+			<ymax>266</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/157.jpg b/PAR 152/cone_dataset/157.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..091b880b24983c8bd34e3c054d81c47b6cdb5186
Binary files /dev/null and b/PAR 152/cone_dataset/157.jpg differ
diff --git a/PAR 152/cone_dataset/157.xml b/PAR 152/cone_dataset/157.xml
new file mode 100644
index 0000000000000000000000000000000000000000..c523b4e61c43637dd169408366a01f17565cc314
--- /dev/null
+++ b/PAR 152/cone_dataset/157.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>157.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\157.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>552</width>
+		<height>312</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>47</xmin>
+			<ymin>1</ymin>
+			<xmax>313</xmax>
+			<ymax>265</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/158.jpg b/PAR 152/cone_dataset/158.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..2e82f8a498c79dc93f15feeff8ac4c0b48de9f97
Binary files /dev/null and b/PAR 152/cone_dataset/158.jpg differ
diff --git a/PAR 152/cone_dataset/158.xml b/PAR 152/cone_dataset/158.xml
new file mode 100644
index 0000000000000000000000000000000000000000..dcf256b50a50c21ed6e789b7297dc1884986fca5
--- /dev/null
+++ b/PAR 152/cone_dataset/158.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>158.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\158.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>1023</width>
+		<height>1024</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>683</xmin>
+			<ymin>420</ymin>
+			<xmax>965</xmax>
+			<ymax>934</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/159.jpg b/PAR 152/cone_dataset/159.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..98fa2d3c3473c37f33e43358236fd770137acb2c
Binary files /dev/null and b/PAR 152/cone_dataset/159.jpg differ
diff --git a/PAR 152/cone_dataset/159.xml b/PAR 152/cone_dataset/159.xml
new file mode 100644
index 0000000000000000000000000000000000000000..c72ca86fe18915da04d2312730fdf0d89cf6ba6a
--- /dev/null
+++ b/PAR 152/cone_dataset/159.xml	
@@ -0,0 +1,86 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>159.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\159.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>339</width>
+		<height>508</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>52</xmin>
+			<ymin>243</ymin>
+			<xmax>150</xmax>
+			<ymax>460</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>172</xmin>
+			<ymin>184</ymin>
+			<xmax>237</xmax>
+			<ymax>315</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>230</xmin>
+			<ymin>152</ymin>
+			<xmax>270</xmax>
+			<ymax>235</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>257</xmin>
+			<ymin>139</ymin>
+			<xmax>285</xmax>
+			<ymax>203</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>279</xmin>
+			<ymin>128</ymin>
+			<xmax>300</xmax>
+			<ymax>180</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>290</xmin>
+			<ymin>126</ymin>
+			<xmax>307</xmax>
+			<ymax>165</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/16.jpg b/PAR 152/cone_dataset/16.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..703d882532a61c920092776e8fd47f3fd6c6c6e4
Binary files /dev/null and b/PAR 152/cone_dataset/16.jpg differ
diff --git a/PAR 152/cone_dataset/16.xml b/PAR 152/cone_dataset/16.xml
new file mode 100644
index 0000000000000000000000000000000000000000..1d7ea701eadfbe5c49172c2da5e89e8bfb6d2b93
--- /dev/null
+++ b/PAR 152/cone_dataset/16.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>16.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\16.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>478</width>
+		<height>359</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>39</xmin>
+			<ymin>101</ymin>
+			<xmax>215</xmax>
+			<ymax>332</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/160.jpg b/PAR 152/cone_dataset/160.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..a010832db78fb36adaa830a736d0fd0d6dcfd2d2
Binary files /dev/null and b/PAR 152/cone_dataset/160.jpg differ
diff --git a/PAR 152/cone_dataset/160.xml b/PAR 152/cone_dataset/160.xml
new file mode 100644
index 0000000000000000000000000000000000000000..088a0165cf948d1d1f5e8cab48e0295fdcb36244
--- /dev/null
+++ b/PAR 152/cone_dataset/160.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>160.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\160.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>479</width>
+		<height>359</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>147</xmin>
+			<ymin>7</ymin>
+			<xmax>258</xmax>
+			<ymax>161</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/161.jpg b/PAR 152/cone_dataset/161.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..b5e38a85bfe2c0b588a1a41e5f5b41f4442481b3
Binary files /dev/null and b/PAR 152/cone_dataset/161.jpg differ
diff --git a/PAR 152/cone_dataset/161.xml b/PAR 152/cone_dataset/161.xml
new file mode 100644
index 0000000000000000000000000000000000000000..4cf83e77f85bfbfbd18a977104831a97931cb6ff
--- /dev/null
+++ b/PAR 152/cone_dataset/161.xml	
@@ -0,0 +1,254 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>161.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\161.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>508</width>
+		<height>339</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>206</xmin>
+			<ymin>125</ymin>
+			<xmax>321</xmax>
+			<ymax>315</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>41</xmin>
+			<ymin>126</ymin>
+			<xmax>139</xmax>
+			<ymax>322</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>154</xmin>
+			<ymin>89</ymin>
+			<xmax>233</xmax>
+			<ymax>213</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>25</xmin>
+			<ymin>89</ymin>
+			<xmax>74</xmax>
+			<ymax>213</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>71</xmin>
+			<ymin>50</ymin>
+			<xmax>119</xmax>
+			<ymax>128</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>1</xmin>
+			<ymin>45</ymin>
+			<xmax>53</xmax>
+			<ymax>119</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>394</xmin>
+			<ymin>121</ymin>
+			<xmax>508</xmax>
+			<ymax>308</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>467</xmin>
+			<ymin>88</ymin>
+			<xmax>508</xmax>
+			<ymax>202</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>346</xmin>
+			<ymin>75</ymin>
+			<xmax>413</xmax>
+			<ymax>182</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>269</xmin>
+			<ymin>66</ymin>
+			<xmax>320</xmax>
+			<ymax>162</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>199</xmin>
+			<ymin>62</ymin>
+			<xmax>252</xmax>
+			<ymax>148</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>136</xmin>
+			<ymin>55</ymin>
+			<xmax>191</xmax>
+			<ymax>136</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>109</xmin>
+			<ymin>19</ymin>
+			<xmax>136</xmax>
+			<ymax>69</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>65</xmin>
+			<ymin>18</ymin>
+			<xmax>91</xmax>
+			<ymax>69</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>24</xmin>
+			<ymin>19</ymin>
+			<xmax>52</xmax>
+			<ymax>69</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>227</xmin>
+			<ymin>22</ymin>
+			<xmax>256</xmax>
+			<ymax>72</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>185</xmin>
+			<ymin>20</ymin>
+			<xmax>213</xmax>
+			<ymax>73</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>367</xmin>
+			<ymin>24</ymin>
+			<xmax>396</xmax>
+			<ymax>77</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>413</xmin>
+			<ymin>23</ymin>
+			<xmax>448</xmax>
+			<ymax>77</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>468</xmin>
+			<ymin>23</ymin>
+			<xmax>501</xmax>
+			<ymax>80</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/162.jpg b/PAR 152/cone_dataset/162.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..505960f9097d1559129a0e12896096a37e93b035
Binary files /dev/null and b/PAR 152/cone_dataset/162.jpg differ
diff --git a/PAR 152/cone_dataset/162.xml b/PAR 152/cone_dataset/162.xml
new file mode 100644
index 0000000000000000000000000000000000000000..a9a71716e4d3ae0c327b3a79fb905601a9d949ed
--- /dev/null
+++ b/PAR 152/cone_dataset/162.xml	
@@ -0,0 +1,110 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>162.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\162.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>339</width>
+		<height>509</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>17</xmin>
+			<ymin>254</ymin>
+			<xmax>57</xmax>
+			<ymax>330</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>60</xmin>
+			<ymin>254</ymin>
+			<xmax>102</xmax>
+			<ymax>332</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>105</xmin>
+			<ymin>252</ymin>
+			<xmax>145</xmax>
+			<ymax>331</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>142</xmin>
+			<ymin>252</ymin>
+			<xmax>186</xmax>
+			<ymax>326</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>186</xmin>
+			<ymin>249</ymin>
+			<xmax>227</xmax>
+			<ymax>325</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>230</xmin>
+			<ymin>248</ymin>
+			<xmax>268</xmax>
+			<ymax>326</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>261</xmin>
+			<ymin>251</ymin>
+			<xmax>305</xmax>
+			<ymax>325</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>300</xmin>
+			<ymin>250</ymin>
+			<xmax>338</xmax>
+			<ymax>321</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/163.jpg b/PAR 152/cone_dataset/163.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..a3829a87450a952900eec7354185c64654c0a08b
Binary files /dev/null and b/PAR 152/cone_dataset/163.jpg differ
diff --git a/PAR 152/cone_dataset/163.xml b/PAR 152/cone_dataset/163.xml
new file mode 100644
index 0000000000000000000000000000000000000000..bbb8815c44081a3f5f46509800216c10ce99cff7
--- /dev/null
+++ b/PAR 152/cone_dataset/163.xml	
@@ -0,0 +1,86 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>163.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\163.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>339</width>
+		<height>509</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>153</xmin>
+			<ymin>404</ymin>
+			<xmax>194</xmax>
+			<ymax>486</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>115</xmin>
+			<ymin>399</ymin>
+			<xmax>152</xmax>
+			<ymax>479</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>187</xmin>
+			<ymin>402</ymin>
+			<xmax>230</xmax>
+			<ymax>480</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>213</xmin>
+			<ymin>401</ymin>
+			<xmax>251</xmax>
+			<ymax>470</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>95</xmin>
+			<ymin>401</ymin>
+			<xmax>122</xmax>
+			<ymax>464</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>185</xmin>
+			<ymin>385</ymin>
+			<xmax>205</xmax>
+			<ymax>445</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/164.jpg b/PAR 152/cone_dataset/164.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..025c84855bc4779cafa1f4ea6a1c31c65953f76c
Binary files /dev/null and b/PAR 152/cone_dataset/164.jpg differ
diff --git a/PAR 152/cone_dataset/164.xml b/PAR 152/cone_dataset/164.xml
new file mode 100644
index 0000000000000000000000000000000000000000..5a62d5b830c0c9e5e7ee205893ed390ad94b3ea3
--- /dev/null
+++ b/PAR 152/cone_dataset/164.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>164.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\164.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>359</width>
+		<height>479</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>201</xmin>
+			<ymin>144</ymin>
+			<xmax>331</xmax>
+			<ymax>406</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/165.jpg b/PAR 152/cone_dataset/165.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..0ac5ff0e69abdbba1b955797d943e014ad90465c
Binary files /dev/null and b/PAR 152/cone_dataset/165.jpg differ
diff --git a/PAR 152/cone_dataset/165.xml b/PAR 152/cone_dataset/165.xml
new file mode 100644
index 0000000000000000000000000000000000000000..a21a1fc6de49057e86ca36ef62260800b66aafd6
--- /dev/null
+++ b/PAR 152/cone_dataset/165.xml	
@@ -0,0 +1,98 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>165.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\165.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>509</width>
+		<height>339</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>200</xmin>
+			<ymin>59</ymin>
+			<xmax>239</xmax>
+			<ymax>156</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>90</xmin>
+			<ymin>175</ymin>
+			<xmax>162</xmax>
+			<ymax>337</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>245</xmin>
+			<ymin>21</ymin>
+			<xmax>271</xmax>
+			<ymax>76</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>456</xmin>
+			<ymin>112</ymin>
+			<xmax>494</xmax>
+			<ymax>234</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>392</xmin>
+			<ymin>50</ymin>
+			<xmax>426</xmax>
+			<ymax>131</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>356</xmin>
+			<ymin>12</ymin>
+			<xmax>377</xmax>
+			<ymax>61</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>344</xmin>
+			<ymin>1</ymin>
+			<xmax>357</xmax>
+			<ymax>32</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/166.jpg b/PAR 152/cone_dataset/166.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..77e5432e7bbb421d9ae0e6da2606cf508d14c3b2
Binary files /dev/null and b/PAR 152/cone_dataset/166.jpg differ
diff --git a/PAR 152/cone_dataset/166.xml b/PAR 152/cone_dataset/166.xml
new file mode 100644
index 0000000000000000000000000000000000000000..c6fe5e9e0ef2e6be4efeb145aadc3a7c25437837
--- /dev/null
+++ b/PAR 152/cone_dataset/166.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>166.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\166.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>509</width>
+		<height>339</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>273</xmin>
+			<ymin>32</ymin>
+			<xmax>396</xmax>
+			<ymax>243</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/167.jpg b/PAR 152/cone_dataset/167.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..8a9814e9c9df5572ecf31ef5f206b5753d61253c
Binary files /dev/null and b/PAR 152/cone_dataset/167.jpg differ
diff --git a/PAR 152/cone_dataset/167.xml b/PAR 152/cone_dataset/167.xml
new file mode 100644
index 0000000000000000000000000000000000000000..5494f8e6c259de10b84ac6e6316eb818d701e5f4
--- /dev/null
+++ b/PAR 152/cone_dataset/167.xml	
@@ -0,0 +1,62 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>167.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\167.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>512</width>
+		<height>336</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>7</xmin>
+			<ymin>150</ymin>
+			<xmax>95</xmax>
+			<ymax>298</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>136</xmin>
+			<ymin>152</ymin>
+			<xmax>205</xmax>
+			<ymax>302</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>338</xmin>
+			<ymin>155</ymin>
+			<xmax>416</xmax>
+			<ymax>300</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>412</xmin>
+			<ymin>158</ymin>
+			<xmax>496</xmax>
+			<ymax>297</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/168.jpg b/PAR 152/cone_dataset/168.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..a899503782fe6140e055e4ee5dbfedb0f6569759
Binary files /dev/null and b/PAR 152/cone_dataset/168.jpg differ
diff --git a/PAR 152/cone_dataset/168.xml b/PAR 152/cone_dataset/168.xml
new file mode 100644
index 0000000000000000000000000000000000000000..ee3ba82bcba8909d3e316f20a573f9dd3b938c52
--- /dev/null
+++ b/PAR 152/cone_dataset/168.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>168.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\168.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>768</width>
+		<height>1024</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>273</xmin>
+			<ymin>421</ymin>
+			<xmax>471</xmax>
+			<ymax>901</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/169.jpg b/PAR 152/cone_dataset/169.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..b0d61bf2708201480161ba1fa9dc68f62ffb22c7
Binary files /dev/null and b/PAR 152/cone_dataset/169.jpg differ
diff --git a/PAR 152/cone_dataset/169.xml b/PAR 152/cone_dataset/169.xml
new file mode 100644
index 0000000000000000000000000000000000000000..a746a6ab81a0f3dc3a075134069c968eb5fda4fc
--- /dev/null
+++ b/PAR 152/cone_dataset/169.xml	
@@ -0,0 +1,50 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>169.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\169.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>359</width>
+		<height>479</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>60</xmin>
+			<ymin>304</ymin>
+			<xmax>174</xmax>
+			<ymax>474</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>160</xmin>
+			<ymin>217</ymin>
+			<xmax>246</xmax>
+			<ymax>356</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>238</xmin>
+			<ymin>164</ymin>
+			<xmax>320</xmax>
+			<ymax>296</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/17.jpg b/PAR 152/cone_dataset/17.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..f4ac7318edb7e8b07d4cc4055d21b9d8040ce91b
Binary files /dev/null and b/PAR 152/cone_dataset/17.jpg differ
diff --git a/PAR 152/cone_dataset/17.xml b/PAR 152/cone_dataset/17.xml
new file mode 100644
index 0000000000000000000000000000000000000000..0917b58198ec496f543687e542bc21fee5f174e0
--- /dev/null
+++ b/PAR 152/cone_dataset/17.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>17.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\17.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>338</width>
+		<height>507</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>63</xmin>
+			<ymin>14</ymin>
+			<xmax>294</xmax>
+			<ymax>490</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/170.jpg b/PAR 152/cone_dataset/170.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..5f40f3a9b42b69d3de097a0156287f1f7e54ab6d
Binary files /dev/null and b/PAR 152/cone_dataset/170.jpg differ
diff --git a/PAR 152/cone_dataset/170.xml b/PAR 152/cone_dataset/170.xml
new file mode 100644
index 0000000000000000000000000000000000000000..93e0899c02040dfe35e4c9bbb47cf8290127bc8a
--- /dev/null
+++ b/PAR 152/cone_dataset/170.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>170.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\170.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>359</width>
+		<height>479</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>237</xmin>
+			<ymin>169</ymin>
+			<xmax>296</xmax>
+			<ymax>283</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/171.jpg b/PAR 152/cone_dataset/171.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..7226ae6c9ee9f01a6358fb55dffbc1bffdb387f5
Binary files /dev/null and b/PAR 152/cone_dataset/171.jpg differ
diff --git a/PAR 152/cone_dataset/171.xml b/PAR 152/cone_dataset/171.xml
new file mode 100644
index 0000000000000000000000000000000000000000..9fe9e59f7b1d484e8db47f98978f1a4c6dca804c
--- /dev/null
+++ b/PAR 152/cone_dataset/171.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>171.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\171.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>359</width>
+		<height>479</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>206</xmin>
+			<ymin>140</ymin>
+			<xmax>336</xmax>
+			<ymax>413</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/172.jpg b/PAR 152/cone_dataset/172.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..36364509b598ce31a9a137c4d8f9493577372452
Binary files /dev/null and b/PAR 152/cone_dataset/172.jpg differ
diff --git a/PAR 152/cone_dataset/172.xml b/PAR 152/cone_dataset/172.xml
new file mode 100644
index 0000000000000000000000000000000000000000..c50e9a089ae8591ba296db14db7561216445147b
--- /dev/null
+++ b/PAR 152/cone_dataset/172.xml	
@@ -0,0 +1,38 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>172.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\172.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>359</width>
+		<height>479</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>204</xmin>
+			<ymin>343</ymin>
+			<xmax>298</xmax>
+			<ymax>477</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>71</xmin>
+			<ymin>2</ymin>
+			<xmax>105</xmax>
+			<ymax>47</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/173.jpg b/PAR 152/cone_dataset/173.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..5a98cdbcded6afe7b6bd5ae46962512403335213
Binary files /dev/null and b/PAR 152/cone_dataset/173.jpg differ
diff --git a/PAR 152/cone_dataset/173.xml b/PAR 152/cone_dataset/173.xml
new file mode 100644
index 0000000000000000000000000000000000000000..ee6b1c23b4ce4d5606f0fd6fcd31d829b23c22ec
--- /dev/null
+++ b/PAR 152/cone_dataset/173.xml	
@@ -0,0 +1,38 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>173.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\173.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>557</width>
+		<height>311</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>61</xmin>
+			<ymin>52</ymin>
+			<xmax>124</xmax>
+			<ymax>139</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>413</xmin>
+			<ymin>45</ymin>
+			<xmax>474</xmax>
+			<ymax>131</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/174.jpg b/PAR 152/cone_dataset/174.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..7e260787f9b2ebe0802eddd07d30e0c0d3195dfa
Binary files /dev/null and b/PAR 152/cone_dataset/174.jpg differ
diff --git a/PAR 152/cone_dataset/174.xml b/PAR 152/cone_dataset/174.xml
new file mode 100644
index 0000000000000000000000000000000000000000..c48ea16e82e6e32945184b4a124fd199ffbdcc9e
--- /dev/null
+++ b/PAR 152/cone_dataset/174.xml	
@@ -0,0 +1,38 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>174.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\174.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>511</width>
+		<height>339</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>232</xmin>
+			<ymin>226</ymin>
+			<xmax>308</xmax>
+			<ymax>334</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>481</xmin>
+			<ymin>213</ymin>
+			<xmax>511</xmax>
+			<ymax>306</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/175.jpg b/PAR 152/cone_dataset/175.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..9066101cf307fc53ade4cdb318329c85841b8e87
Binary files /dev/null and b/PAR 152/cone_dataset/175.jpg differ
diff --git a/PAR 152/cone_dataset/175.xml b/PAR 152/cone_dataset/175.xml
new file mode 100644
index 0000000000000000000000000000000000000000..300f112bd22eee858f11f2ce4cd8f4483ae9941d
--- /dev/null
+++ b/PAR 152/cone_dataset/175.xml	
@@ -0,0 +1,38 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>175.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\175.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>338</width>
+		<height>507</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>100</xmin>
+			<ymin>223</ymin>
+			<xmax>263</xmax>
+			<ymax>439</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>51</xmin>
+			<ymin>26</ymin>
+			<xmax>258</xmax>
+			<ymax>188</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/176.jpg b/PAR 152/cone_dataset/176.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..4ba75b112680b148634fd2b60ee2994ab231f0fb
Binary files /dev/null and b/PAR 152/cone_dataset/176.jpg differ
diff --git a/PAR 152/cone_dataset/176.xml b/PAR 152/cone_dataset/176.xml
new file mode 100644
index 0000000000000000000000000000000000000000..b5ff0834e0b3e5b442d87eae4106a92641ede84b
--- /dev/null
+++ b/PAR 152/cone_dataset/176.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>176.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\176.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>479</width>
+		<height>359</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>83</xmin>
+			<ymin>170</ymin>
+			<xmax>169</xmax>
+			<ymax>302</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/177.jpg b/PAR 152/cone_dataset/177.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..b3746c625648295b70a87f78c2e02a2ace8a52f0
Binary files /dev/null and b/PAR 152/cone_dataset/177.jpg differ
diff --git a/PAR 152/cone_dataset/177.xml b/PAR 152/cone_dataset/177.xml
new file mode 100644
index 0000000000000000000000000000000000000000..2db0a0aa4944d025381e9c230f64877116ff7e77
--- /dev/null
+++ b/PAR 152/cone_dataset/177.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>177.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\177.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>1024</width>
+		<height>1024</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>360</xmin>
+			<ymin>314</ymin>
+			<xmax>649</xmax>
+			<ymax>734</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/178.jpg b/PAR 152/cone_dataset/178.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..53dbe6ca95812684a7b209e840c9b9d698ae703c
Binary files /dev/null and b/PAR 152/cone_dataset/178.jpg differ
diff --git a/PAR 152/cone_dataset/178.xml b/PAR 152/cone_dataset/178.xml
new file mode 100644
index 0000000000000000000000000000000000000000..5d23130a2887603ed8eb5caaee9298b92a7dd6f1
--- /dev/null
+++ b/PAR 152/cone_dataset/178.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>178.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\178.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>371</width>
+		<height>464</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>232</xmin>
+			<ymin>235</ymin>
+			<xmax>347</xmax>
+			<ymax>416</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/179.jpg b/PAR 152/cone_dataset/179.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..a8cc559a6c1356a74f5d294f108eee2366467529
Binary files /dev/null and b/PAR 152/cone_dataset/179.jpg differ
diff --git a/PAR 152/cone_dataset/179.xml b/PAR 152/cone_dataset/179.xml
new file mode 100644
index 0000000000000000000000000000000000000000..bd6fa60952d69400e9a9eabbfd9a21411bd15951
--- /dev/null
+++ b/PAR 152/cone_dataset/179.xml	
@@ -0,0 +1,122 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>179.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\179.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>512</width>
+		<height>335</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>198</xmin>
+			<ymin>204</ymin>
+			<xmax>279</xmax>
+			<ymax>333</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>356</xmin>
+			<ymin>161</ymin>
+			<xmax>432</xmax>
+			<ymax>279</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>397</xmin>
+			<ymin>109</ymin>
+			<xmax>455</xmax>
+			<ymax>211</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>375</xmin>
+			<ymin>48</ymin>
+			<xmax>420</xmax>
+			<ymax>125</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>250</xmin>
+			<ymin>8</ymin>
+			<xmax>290</xmax>
+			<ymax>69</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>325</xmin>
+			<ymin>58</ymin>
+			<xmax>375</xmax>
+			<ymax>100</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>145</xmin>
+			<ymin>29</ymin>
+			<xmax>197</xmax>
+			<ymax>97</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>43</xmin>
+			<ymin>83</ymin>
+			<xmax>103</xmax>
+			<ymax>177</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>91</xmin>
+			<ymin>202</ymin>
+			<xmax>165</xmax>
+			<ymax>286</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/18.jpg b/PAR 152/cone_dataset/18.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..137d4ed358a82804098b3e63ce8f1fcf85124ded
Binary files /dev/null and b/PAR 152/cone_dataset/18.jpg differ
diff --git a/PAR 152/cone_dataset/18.xml b/PAR 152/cone_dataset/18.xml
new file mode 100644
index 0000000000000000000000000000000000000000..e68fd03950d2b0ce96d1a6c738dfa1b43c73d52e
--- /dev/null
+++ b/PAR 152/cone_dataset/18.xml	
@@ -0,0 +1,50 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>18.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\18.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>338</width>
+		<height>507</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>1</xmin>
+			<ymin>82</ymin>
+			<xmax>164</xmax>
+			<ymax>499</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>39</xmin>
+			<ymin>1</ymin>
+			<xmax>169</xmax>
+			<ymax>221</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>148</xmin>
+			<ymin>1</ymin>
+			<xmax>227</xmax>
+			<ymax>128</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/180.jpg b/PAR 152/cone_dataset/180.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..7b5658c234354a15c87f98ee8dd60c36e877fb46
Binary files /dev/null and b/PAR 152/cone_dataset/180.jpg differ
diff --git a/PAR 152/cone_dataset/180.xml b/PAR 152/cone_dataset/180.xml
new file mode 100644
index 0000000000000000000000000000000000000000..345f2e21caf998cfc9fe9f7767720238915be188
--- /dev/null
+++ b/PAR 152/cone_dataset/180.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>180.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\180.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>326</width>
+		<height>527</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>27</xmin>
+			<ymin>209</ymin>
+			<xmax>154</xmax>
+			<ymax>481</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/181.jpg b/PAR 152/cone_dataset/181.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..11e3bb0cdae4e089078f7df2dc97461d72633e76
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diff --git a/PAR 152/cone_dataset/181.xml b/PAR 152/cone_dataset/181.xml
new file mode 100644
index 0000000000000000000000000000000000000000..1cb0ae43293c25570965af86ba919c3e2aaa893b
--- /dev/null
+++ b/PAR 152/cone_dataset/181.xml	
@@ -0,0 +1,38 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>181.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\181.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>359</width>
+		<height>479</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>156</xmin>
+			<ymin>366</ymin>
+			<xmax>201</xmax>
+			<ymax>463</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>265</xmin>
+			<ymin>372</ymin>
+			<xmax>306</xmax>
+			<ymax>467</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/182.jpg b/PAR 152/cone_dataset/182.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..cd5978f179400ccb4696ed50c07536c509b9508d
Binary files /dev/null and b/PAR 152/cone_dataset/182.jpg differ
diff --git a/PAR 152/cone_dataset/182.xml b/PAR 152/cone_dataset/182.xml
new file mode 100644
index 0000000000000000000000000000000000000000..3da8b19d8b5d939ef76cabf3acf8c98c3611427d
--- /dev/null
+++ b/PAR 152/cone_dataset/182.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>182.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\182.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>359</width>
+		<height>479</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>175</xmin>
+			<ymin>42</ymin>
+			<xmax>292</xmax>
+			<ymax>277</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/183.jpg b/PAR 152/cone_dataset/183.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..68aaefaeff0afc8f324d69e56a69cd78986a1d7a
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diff --git a/PAR 152/cone_dataset/183.xml b/PAR 152/cone_dataset/183.xml
new file mode 100644
index 0000000000000000000000000000000000000000..d2e7a0d193db3e42bcc0f507bccac9e3d3c25f5c
--- /dev/null
+++ b/PAR 152/cone_dataset/183.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>183.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\183.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>344</width>
+		<height>502</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>130</xmin>
+			<ymin>151</ymin>
+			<xmax>282</xmax>
+			<ymax>426</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/184.jpg b/PAR 152/cone_dataset/184.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..137d4ed358a82804098b3e63ce8f1fcf85124ded
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diff --git a/PAR 152/cone_dataset/184.xml b/PAR 152/cone_dataset/184.xml
new file mode 100644
index 0000000000000000000000000000000000000000..12c9406e42884d12edac7ee08ae37054a8f4d8d5
--- /dev/null
+++ b/PAR 152/cone_dataset/184.xml	
@@ -0,0 +1,50 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>184.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\184.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>338</width>
+		<height>507</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>1</xmin>
+			<ymin>82</ymin>
+			<xmax>165</xmax>
+			<ymax>502</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>82</xmin>
+			<ymin>1</ymin>
+			<xmax>165</xmax>
+			<ymax>219</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>151</xmin>
+			<ymin>1</ymin>
+			<xmax>225</xmax>
+			<ymax>129</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/185.jpg b/PAR 152/cone_dataset/185.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..f825fc5cf9f69c10b9595ad810d20f10ee637680
Binary files /dev/null and b/PAR 152/cone_dataset/185.jpg differ
diff --git a/PAR 152/cone_dataset/185.xml b/PAR 152/cone_dataset/185.xml
new file mode 100644
index 0000000000000000000000000000000000000000..2ae38611e918f26c577d6d5a55ec633ea7e49f96
--- /dev/null
+++ b/PAR 152/cone_dataset/185.xml	
@@ -0,0 +1,50 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>185.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\185.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>506</width>
+		<height>338</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>251</xmin>
+			<ymin>152</ymin>
+			<xmax>302</xmax>
+			<ymax>235</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>246</xmin>
+			<ymin>149</ymin>
+			<xmax>272</xmax>
+			<ymax>209</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>242</xmin>
+			<ymin>145</ymin>
+			<xmax>258</xmax>
+			<ymax>184</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/186.jpg b/PAR 152/cone_dataset/186.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..5654cf886f0d44f5f43ee40f9a15683c8a5c0706
Binary files /dev/null and b/PAR 152/cone_dataset/186.jpg differ
diff --git a/PAR 152/cone_dataset/186.xml b/PAR 152/cone_dataset/186.xml
new file mode 100644
index 0000000000000000000000000000000000000000..1b9caedcfd4b05dd79f03205add860bcbbbb4794
--- /dev/null
+++ b/PAR 152/cone_dataset/186.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>186.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\186.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>338</width>
+		<height>507</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>154</xmin>
+			<ymin>205</ymin>
+			<xmax>284</xmax>
+			<ymax>423</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/187.jpg b/PAR 152/cone_dataset/187.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..ec74d13270a513f15c37d09ebb087a75e9555e29
Binary files /dev/null and b/PAR 152/cone_dataset/187.jpg differ
diff --git a/PAR 152/cone_dataset/187.xml b/PAR 152/cone_dataset/187.xml
new file mode 100644
index 0000000000000000000000000000000000000000..41fac624c56a6863c634e82e4b65467988bad2fb
--- /dev/null
+++ b/PAR 152/cone_dataset/187.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>187.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\187.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>338</width>
+		<height>507</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>96</xmin>
+			<ymin>64</ymin>
+			<xmax>232</xmax>
+			<ymax>283</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/188.jpg b/PAR 152/cone_dataset/188.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..f8041c5ea153e8fd9136097da848730b78a06749
Binary files /dev/null and b/PAR 152/cone_dataset/188.jpg differ
diff --git a/PAR 152/cone_dataset/188.xml b/PAR 152/cone_dataset/188.xml
new file mode 100644
index 0000000000000000000000000000000000000000..86fe7763385afe643821471b39f8de442412e285
--- /dev/null
+++ b/PAR 152/cone_dataset/188.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>188.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\188.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>350</width>
+		<height>490</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>60</xmin>
+			<ymin>36</ymin>
+			<xmax>291</xmax>
+			<ymax>457</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/189.jpg b/PAR 152/cone_dataset/189.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..5e309ebc1ea1342283a2d6c35f6cf24fdb3c6b19
Binary files /dev/null and b/PAR 152/cone_dataset/189.jpg differ
diff --git a/PAR 152/cone_dataset/189.xml b/PAR 152/cone_dataset/189.xml
new file mode 100644
index 0000000000000000000000000000000000000000..331abb6107d0602e667d296443c3b6607dcd758e
--- /dev/null
+++ b/PAR 152/cone_dataset/189.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>189.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\189.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>509</width>
+		<height>336</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>351</xmin>
+			<ymin>214</ymin>
+			<xmax>417</xmax>
+			<ymax>300</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/19.jpg b/PAR 152/cone_dataset/19.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..1b9fbe5d2024e13bcecd606c29c13e89403c123d
Binary files /dev/null and b/PAR 152/cone_dataset/19.jpg differ
diff --git a/PAR 152/cone_dataset/19.xml b/PAR 152/cone_dataset/19.xml
new file mode 100644
index 0000000000000000000000000000000000000000..9a03aee7b077abf3840bfca95d273e625ff36863
--- /dev/null
+++ b/PAR 152/cone_dataset/19.xml	
@@ -0,0 +1,62 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>19.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\19.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>507</width>
+		<height>338</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>73</xmin>
+			<ymin>48</ymin>
+			<xmax>258</xmax>
+			<ymax>314</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>168</xmin>
+			<ymin>59</ymin>
+			<xmax>232</xmax>
+			<ymax>189</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>201</xmin>
+			<ymin>61</ymin>
+			<xmax>251</xmax>
+			<ymax>147</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>224</xmin>
+			<ymin>63</ymin>
+			<xmax>255</xmax>
+			<ymax>116</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/190.jpg b/PAR 152/cone_dataset/190.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..d2ce68d9b18fdc4d8909ce6debe126ad21ec9238
Binary files /dev/null and b/PAR 152/cone_dataset/190.jpg differ
diff --git a/PAR 152/cone_dataset/190.xml b/PAR 152/cone_dataset/190.xml
new file mode 100644
index 0000000000000000000000000000000000000000..cfb3760d0a0fce8a8be2782a0a593eef288e78f0
--- /dev/null
+++ b/PAR 152/cone_dataset/190.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>190.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\190.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>478</width>
+		<height>359</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>163</xmin>
+			<ymin>64</ymin>
+			<xmax>294</xmax>
+			<ymax>269</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/191.jpg b/PAR 152/cone_dataset/191.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..177c061f72d2f47bf448baf52e05fd126ee9e2f9
Binary files /dev/null and b/PAR 152/cone_dataset/191.jpg differ
diff --git a/PAR 152/cone_dataset/191.xml b/PAR 152/cone_dataset/191.xml
new file mode 100644
index 0000000000000000000000000000000000000000..67436a77eb4983aa4da37b27feebada147c9e6e6
--- /dev/null
+++ b/PAR 152/cone_dataset/191.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>191.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\191.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>478</width>
+		<height>359</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>255</xmin>
+			<ymin>205</ymin>
+			<xmax>314</xmax>
+			<ymax>330</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/192.jpg b/PAR 152/cone_dataset/192.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..a97bdb190855974ba9c5acded6ce48bf44a68452
Binary files /dev/null and b/PAR 152/cone_dataset/192.jpg differ
diff --git a/PAR 152/cone_dataset/192.xml b/PAR 152/cone_dataset/192.xml
new file mode 100644
index 0000000000000000000000000000000000000000..929c2f4d3fa28ab750eb31485ae95a3aed8e05f0
--- /dev/null
+++ b/PAR 152/cone_dataset/192.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>192.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\192.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>359</width>
+		<height>478</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>74</xmin>
+			<ymin>76</ymin>
+			<xmax>171</xmax>
+			<ymax>260</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/193.jpg b/PAR 152/cone_dataset/193.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..9619a5e785f2ae0316a7b08a629529876b266897
Binary files /dev/null and b/PAR 152/cone_dataset/193.jpg differ
diff --git a/PAR 152/cone_dataset/193.xml b/PAR 152/cone_dataset/193.xml
new file mode 100644
index 0000000000000000000000000000000000000000..e6d4abad87e78d4e9b771f883fec3f22ce068989
--- /dev/null
+++ b/PAR 152/cone_dataset/193.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>193.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\193.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>338</width>
+		<height>507</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>116</xmin>
+			<ymin>52</ymin>
+			<xmax>210</xmax>
+			<ymax>276</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/194.jpg b/PAR 152/cone_dataset/194.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..cc34ec1c76bb646ff905916d1ae753683a48068e
Binary files /dev/null and b/PAR 152/cone_dataset/194.jpg differ
diff --git a/PAR 152/cone_dataset/194.xml b/PAR 152/cone_dataset/194.xml
new file mode 100644
index 0000000000000000000000000000000000000000..5634147d3a799331d03b94a21cfbabbdf869ee5c
--- /dev/null
+++ b/PAR 152/cone_dataset/194.xml	
@@ -0,0 +1,50 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>194.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\194.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>359</width>
+		<height>479</height>
+		<depth>1</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>165</xmin>
+			<ymin>28</ymin>
+			<xmax>243</xmax>
+			<ymax>158</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>267</xmin>
+			<ymin>150</ymin>
+			<xmax>359</xmax>
+			<ymax>430</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>151</xmin>
+			<ymin>1</ymin>
+			<xmax>189</xmax>
+			<ymax>85</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/195.jpg b/PAR 152/cone_dataset/195.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..efd660f7362b535c03ab26991da95b4626ead170
Binary files /dev/null and b/PAR 152/cone_dataset/195.jpg differ
diff --git a/PAR 152/cone_dataset/195.xml b/PAR 152/cone_dataset/195.xml
new file mode 100644
index 0000000000000000000000000000000000000000..07914ca283422ac22138fe03f02c0644b962d834
--- /dev/null
+++ b/PAR 152/cone_dataset/195.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>195.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\195.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>507</width>
+		<height>338</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>133</xmin>
+			<ymin>102</ymin>
+			<xmax>235</xmax>
+			<ymax>257</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/196.jpg b/PAR 152/cone_dataset/196.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..1b61c7543d8887c3422610b9367bc2dd0a2eff32
Binary files /dev/null and b/PAR 152/cone_dataset/196.jpg differ
diff --git a/PAR 152/cone_dataset/196.xml b/PAR 152/cone_dataset/196.xml
new file mode 100644
index 0000000000000000000000000000000000000000..dce0d9a6d7da98e8ecbd92b5af297b3c8dead642
--- /dev/null
+++ b/PAR 152/cone_dataset/196.xml	
@@ -0,0 +1,38 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>196.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\196.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>507</width>
+		<height>338</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>74</xmin>
+			<ymin>115</ymin>
+			<xmax>177</xmax>
+			<ymax>315</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>213</xmin>
+			<ymin>130</ymin>
+			<xmax>256</xmax>
+			<ymax>196</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/197.jpg b/PAR 152/cone_dataset/197.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..62aed578b6926f876904ba0854cc07c881ba9ab6
Binary files /dev/null and b/PAR 152/cone_dataset/197.jpg differ
diff --git a/PAR 152/cone_dataset/197.xml b/PAR 152/cone_dataset/197.xml
new file mode 100644
index 0000000000000000000000000000000000000000..88b9f33b48426890a95bccf97d48eab8f04303a9
--- /dev/null
+++ b/PAR 152/cone_dataset/197.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>197.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\197.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>338</width>
+		<height>507</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>184</xmin>
+			<ymin>63</ymin>
+			<xmax>289</xmax>
+			<ymax>256</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/198.jpg b/PAR 152/cone_dataset/198.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..3d1225e8131b8139e0cc01b501bfb2df90ab5a59
Binary files /dev/null and b/PAR 152/cone_dataset/198.jpg differ
diff --git a/PAR 152/cone_dataset/198.xml b/PAR 152/cone_dataset/198.xml
new file mode 100644
index 0000000000000000000000000000000000000000..78e9cbbf303b8ac941773990a7fcece877cce0d1
--- /dev/null
+++ b/PAR 152/cone_dataset/198.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>198.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\198.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>359</width>
+		<height>479</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>180</xmin>
+			<ymin>306</ymin>
+			<xmax>244</xmax>
+			<ymax>412</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/199.jpg b/PAR 152/cone_dataset/199.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..7e10982a0c453f90bb81366ff7aa5e1397e2644c
Binary files /dev/null and b/PAR 152/cone_dataset/199.jpg differ
diff --git a/PAR 152/cone_dataset/199.xml b/PAR 152/cone_dataset/199.xml
new file mode 100644
index 0000000000000000000000000000000000000000..8803d31f786dd0ac7956a86029d9ae971d57930d
--- /dev/null
+++ b/PAR 152/cone_dataset/199.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>199.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\199.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>347</width>
+		<height>491</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>45</xmin>
+			<ymin>128</ymin>
+			<xmax>240</xmax>
+			<ymax>453</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/2.jpg b/PAR 152/cone_dataset/2.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..4020175b16c33a75fa04c4b2644388486a0a7cba
Binary files /dev/null and b/PAR 152/cone_dataset/2.jpg differ
diff --git a/PAR 152/cone_dataset/2.xml b/PAR 152/cone_dataset/2.xml
new file mode 100644
index 0000000000000000000000000000000000000000..4f130d65a364588d87b526e4cb544e5206e50ec3
--- /dev/null
+++ b/PAR 152/cone_dataset/2.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>2.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\2.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>397</width>
+		<height>432</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>63</xmin>
+			<ymin>36</ymin>
+			<xmax>341</xmax>
+			<ymax>374</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/20.jpg b/PAR 152/cone_dataset/20.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..9066101cf307fc53ade4cdb318329c85841b8e87
Binary files /dev/null and b/PAR 152/cone_dataset/20.jpg differ
diff --git a/PAR 152/cone_dataset/20.xml b/PAR 152/cone_dataset/20.xml
new file mode 100644
index 0000000000000000000000000000000000000000..9cf2d551759cd629cfcc0fafd64c93a3cc67cfaa
--- /dev/null
+++ b/PAR 152/cone_dataset/20.xml	
@@ -0,0 +1,38 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>20.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\20.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>338</width>
+		<height>507</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>103</xmin>
+			<ymin>222</ymin>
+			<xmax>266</xmax>
+			<ymax>432</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>55</xmin>
+			<ymin>27</ymin>
+			<xmax>259</xmax>
+			<ymax>191</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/200.jpg b/PAR 152/cone_dataset/200.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..77bf49165b4049c10816316e75f2f33b170f7ad8
Binary files /dev/null and b/PAR 152/cone_dataset/200.jpg differ
diff --git a/PAR 152/cone_dataset/200.xml b/PAR 152/cone_dataset/200.xml
new file mode 100644
index 0000000000000000000000000000000000000000..f03057c7db1ca714d0a170a4003286424a041eef
--- /dev/null
+++ b/PAR 152/cone_dataset/200.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>200.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\200.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>359</width>
+		<height>479</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>123</xmin>
+			<ymin>257</ymin>
+			<xmax>226</xmax>
+			<ymax>399</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/201.jpg b/PAR 152/cone_dataset/201.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..1c065130322855e89138fcac64b97ae687d06434
Binary files /dev/null and b/PAR 152/cone_dataset/201.jpg differ
diff --git a/PAR 152/cone_dataset/201.xml b/PAR 152/cone_dataset/201.xml
new file mode 100644
index 0000000000000000000000000000000000000000..097e67470ec816cc3265961bd71ba480116908bc
--- /dev/null
+++ b/PAR 152/cone_dataset/201.xml	
@@ -0,0 +1,86 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>201.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\201.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>507</width>
+		<height>337</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>360</xmin>
+			<ymin>41</ymin>
+			<xmax>411</xmax>
+			<ymax>120</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>473</xmin>
+			<ymin>32</ymin>
+			<xmax>507</xmax>
+			<ymax>119</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>306</xmin>
+			<ymin>37</ymin>
+			<xmax>350</xmax>
+			<ymax>107</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>318</xmin>
+			<ymin>2</ymin>
+			<xmax>356</xmax>
+			<ymax>59</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>401</xmin>
+			<ymin>1</ymin>
+			<xmax>436</xmax>
+			<ymax>27</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>296</xmin>
+			<ymin>22</ymin>
+			<xmax>312</xmax>
+			<ymax>88</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/202.jpg b/PAR 152/cone_dataset/202.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..c15fcad3f347e27c50fe0512002737810a7a2904
Binary files /dev/null and b/PAR 152/cone_dataset/202.jpg differ
diff --git a/PAR 152/cone_dataset/202.xml b/PAR 152/cone_dataset/202.xml
new file mode 100644
index 0000000000000000000000000000000000000000..6c71d6d8fb6a8e6e330cfe051f5e1708c7517edc
--- /dev/null
+++ b/PAR 152/cone_dataset/202.xml	
@@ -0,0 +1,38 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>202.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\202.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>509</width>
+		<height>339</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>259</xmin>
+			<ymin>117</ymin>
+			<xmax>388</xmax>
+			<ymax>338</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>365</xmin>
+			<ymin>149</ymin>
+			<xmax>509</xmax>
+			<ymax>339</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/203.jpg b/PAR 152/cone_dataset/203.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..d245687b65d857ce12138abd7182b8c86b4e0a03
Binary files /dev/null and b/PAR 152/cone_dataset/203.jpg differ
diff --git a/PAR 152/cone_dataset/203.xml b/PAR 152/cone_dataset/203.xml
new file mode 100644
index 0000000000000000000000000000000000000000..d9d68e8000dfdc5f0dce5970ebd87dc32ebf2a2f
--- /dev/null
+++ b/PAR 152/cone_dataset/203.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>203.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\203.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>553</width>
+		<height>312</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>306</xmin>
+			<ymin>52</ymin>
+			<xmax>449</xmax>
+			<ymax>189</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/204.jpg b/PAR 152/cone_dataset/204.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..65159409ae6dd3ca8ff930366f80ba79842571b7
Binary files /dev/null and b/PAR 152/cone_dataset/204.jpg differ
diff --git a/PAR 152/cone_dataset/204.xml b/PAR 152/cone_dataset/204.xml
new file mode 100644
index 0000000000000000000000000000000000000000..08873bbff8f5021aa7e738b86e1e1f1427ef8752
--- /dev/null
+++ b/PAR 152/cone_dataset/204.xml	
@@ -0,0 +1,266 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>204.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\204.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>509</width>
+		<height>339</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>171</xmin>
+			<ymin>267</ymin>
+			<xmax>185</xmax>
+			<ymax>295</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>337</xmin>
+			<ymin>279</ymin>
+			<xmax>360</xmax>
+			<ymax>322</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>308</xmin>
+			<ymin>264</ymin>
+			<xmax>326</xmax>
+			<ymax>292</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>127</xmin>
+			<ymin>281</ymin>
+			<xmax>150</xmax>
+			<ymax>323</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>105</xmin>
+			<ymin>280</ymin>
+			<xmax>125</xmax>
+			<ymax>321</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>461</xmin>
+			<ymin>260</ymin>
+			<xmax>479</xmax>
+			<ymax>288</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>433</xmin>
+			<ymin>254</ymin>
+			<xmax>449</xmax>
+			<ymax>280</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>412</xmin>
+			<ymin>253</ymin>
+			<xmax>426</xmax>
+			<ymax>275</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>395</xmin>
+			<ymin>250</ymin>
+			<xmax>407</xmax>
+			<ymax>271</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>371</xmin>
+			<ymin>247</ymin>
+			<xmax>381</xmax>
+			<ymax>265</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>494</xmin>
+			<ymin>250</ymin>
+			<xmax>507</xmax>
+			<ymax>270</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>456</xmin>
+			<ymin>291</ymin>
+			<xmax>493</xmax>
+			<ymax>339</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>318</xmin>
+			<ymin>298</ymin>
+			<xmax>337</xmax>
+			<ymax>339</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>170</xmin>
+			<ymin>296</ymin>
+			<xmax>190</xmax>
+			<ymax>339</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>194</xmin>
+			<ymin>259</ymin>
+			<xmax>205</xmax>
+			<ymax>281</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>363</xmin>
+			<ymin>244</ymin>
+			<xmax>369</xmax>
+			<ymax>262</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>295</xmin>
+			<ymin>254</ymin>
+			<xmax>302</xmax>
+			<ymax>273</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>151</xmin>
+			<ymin>272</ymin>
+			<xmax>169</xmax>
+			<ymax>306</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>20</xmin>
+			<ymin>301</ymin>
+			<xmax>43</xmax>
+			<ymax>339</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>62</xmin>
+			<ymin>290</ymin>
+			<xmax>84</xmax>
+			<ymax>339</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>365</xmin>
+			<ymin>297</ymin>
+			<xmax>387</xmax>
+			<ymax>339</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/205.jpg b/PAR 152/cone_dataset/205.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..a9d316bac6ac8a3b51cd631e812fc160f62b1615
Binary files /dev/null and b/PAR 152/cone_dataset/205.jpg differ
diff --git a/PAR 152/cone_dataset/205.xml b/PAR 152/cone_dataset/205.xml
new file mode 100644
index 0000000000000000000000000000000000000000..606bf836a24adb8e2af4f8a15eb3b48f3be35bcd
--- /dev/null
+++ b/PAR 152/cone_dataset/205.xml	
@@ -0,0 +1,134 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>205.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\205.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>507</width>
+		<height>338</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>10</xmin>
+			<ymin>234</ymin>
+			<xmax>45</xmax>
+			<ymax>272</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>63</xmin>
+			<ymin>234</ymin>
+			<xmax>95</xmax>
+			<ymax>271</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>114</xmin>
+			<ymin>234</ymin>
+			<xmax>146</xmax>
+			<ymax>269</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>165</xmin>
+			<ymin>234</ymin>
+			<xmax>195</xmax>
+			<ymax>268</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>216</xmin>
+			<ymin>231</ymin>
+			<xmax>245</xmax>
+			<ymax>267</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>264</xmin>
+			<ymin>233</ymin>
+			<xmax>296</xmax>
+			<ymax>268</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>316</xmin>
+			<ymin>232</ymin>
+			<xmax>347</xmax>
+			<ymax>268</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>367</xmin>
+			<ymin>230</ymin>
+			<xmax>399</xmax>
+			<ymax>268</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>419</xmin>
+			<ymin>230</ymin>
+			<xmax>451</xmax>
+			<ymax>265</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>469</xmin>
+			<ymin>231</ymin>
+			<xmax>504</xmax>
+			<ymax>265</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/206.jpg b/PAR 152/cone_dataset/206.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..9066101cf307fc53ade4cdb318329c85841b8e87
Binary files /dev/null and b/PAR 152/cone_dataset/206.jpg differ
diff --git a/PAR 152/cone_dataset/206.xml b/PAR 152/cone_dataset/206.xml
new file mode 100644
index 0000000000000000000000000000000000000000..427a09ee1338b4a3516c8b2adf7ce255f9cdc190
--- /dev/null
+++ b/PAR 152/cone_dataset/206.xml	
@@ -0,0 +1,38 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>206.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\206.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>338</width>
+		<height>507</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>104</xmin>
+			<ymin>218</ymin>
+			<xmax>263</xmax>
+			<ymax>435</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>54</xmin>
+			<ymin>30</ymin>
+			<xmax>259</xmax>
+			<ymax>190</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/207.jpg b/PAR 152/cone_dataset/207.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..d33b3283369fb734acd8372ab42dcd7a9aa492b4
Binary files /dev/null and b/PAR 152/cone_dataset/207.jpg differ
diff --git a/PAR 152/cone_dataset/207.xml b/PAR 152/cone_dataset/207.xml
new file mode 100644
index 0000000000000000000000000000000000000000..31b466c59f6b9d6c1d0591cf48f97cb58687d2ab
--- /dev/null
+++ b/PAR 152/cone_dataset/207.xml	
@@ -0,0 +1,62 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>207.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\207.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>510</width>
+		<height>339</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>252</xmin>
+			<ymin>195</ymin>
+			<xmax>299</xmax>
+			<ymax>277</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>387</xmin>
+			<ymin>186</ymin>
+			<xmax>433</xmax>
+			<ymax>260</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>449</xmin>
+			<ymin>183</ymin>
+			<xmax>492</xmax>
+			<ymax>250</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>227</xmin>
+			<ymin>218</ymin>
+			<xmax>247</xmax>
+			<ymax>271</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/208.jpg b/PAR 152/cone_dataset/208.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..5790ebf75ef8081af78ba50c26f48ef9de33b270
Binary files /dev/null and b/PAR 152/cone_dataset/208.jpg differ
diff --git a/PAR 152/cone_dataset/208.xml b/PAR 152/cone_dataset/208.xml
new file mode 100644
index 0000000000000000000000000000000000000000..eaea94d9dfeb48df25755c1f913666ae6693c2f4
--- /dev/null
+++ b/PAR 152/cone_dataset/208.xml	
@@ -0,0 +1,74 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>208.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\208.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>507</width>
+		<height>338</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>20</xmin>
+			<ymin>157</ymin>
+			<xmax>92</xmax>
+			<ymax>246</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>188</xmin>
+			<ymin>97</ymin>
+			<xmax>242</xmax>
+			<ymax>161</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>314</xmin>
+			<ymin>56</ymin>
+			<xmax>355</xmax>
+			<ymax>108</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>406</xmin>
+			<ymin>29</ymin>
+			<xmax>441</xmax>
+			<ymax>73</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>465</xmin>
+			<ymin>8</ymin>
+			<xmax>494</xmax>
+			<ymax>45</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/209.jpg b/PAR 152/cone_dataset/209.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..21c63b5a634945a9dc5b2cef65316396cbebd0e5
Binary files /dev/null and b/PAR 152/cone_dataset/209.jpg differ
diff --git a/PAR 152/cone_dataset/209.xml b/PAR 152/cone_dataset/209.xml
new file mode 100644
index 0000000000000000000000000000000000000000..7ec9a42458eb7b3406ae89335df6d0cad6c4a601
--- /dev/null
+++ b/PAR 152/cone_dataset/209.xml	
@@ -0,0 +1,74 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>209.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\209.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>341</width>
+		<height>502</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>1</xmin>
+			<ymin>245</ymin>
+			<xmax>124</xmax>
+			<ymax>430</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>115</xmin>
+			<ymin>102</ymin>
+			<xmax>210</xmax>
+			<ymax>203</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>175</xmin>
+			<ymin>41</ymin>
+			<xmax>232</xmax>
+			<ymax>110</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>231</xmin>
+			<ymin>15</ymin>
+			<xmax>278</xmax>
+			<ymax>74</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>267</xmin>
+			<ymin>1</ymin>
+			<xmax>309</xmax>
+			<ymax>39</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/21.jpg b/PAR 152/cone_dataset/21.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..c722cf295782b862d6b6483643313b64db95385c
Binary files /dev/null and b/PAR 152/cone_dataset/21.jpg differ
diff --git a/PAR 152/cone_dataset/21.xml b/PAR 152/cone_dataset/21.xml
new file mode 100644
index 0000000000000000000000000000000000000000..ecca5bab9afbe365f9f650d7884c5fcee8ec5f4e
--- /dev/null
+++ b/PAR 152/cone_dataset/21.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>21.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\21.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>337</width>
+		<height>508</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>14</xmin>
+			<ymin>9</ymin>
+			<xmax>320</xmax>
+			<ymax>477</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/210.jpg b/PAR 152/cone_dataset/210.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..a6d22867024397988245f14b95407f297588d192
Binary files /dev/null and b/PAR 152/cone_dataset/210.jpg differ
diff --git a/PAR 152/cone_dataset/210.xml b/PAR 152/cone_dataset/210.xml
new file mode 100644
index 0000000000000000000000000000000000000000..43c7ce58b63137a2404f720ee833d879d0e14690
--- /dev/null
+++ b/PAR 152/cone_dataset/210.xml	
@@ -0,0 +1,50 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>210.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\210.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>359</width>
+		<height>479</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>109</xmin>
+			<ymin>68</ymin>
+			<xmax>169</xmax>
+			<ymax>174</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>215</xmin>
+			<ymin>181</ymin>
+			<xmax>340</xmax>
+			<ymax>302</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>135</xmin>
+			<ymin>141</ymin>
+			<xmax>224</xmax>
+			<ymax>244</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/211.jpg b/PAR 152/cone_dataset/211.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..6544e6dfd053b4d476edb01a89462d67f0397b46
Binary files /dev/null and b/PAR 152/cone_dataset/211.jpg differ
diff --git a/PAR 152/cone_dataset/211.xml b/PAR 152/cone_dataset/211.xml
new file mode 100644
index 0000000000000000000000000000000000000000..a68e768a75174393828432202b8f90095f5ef2e1
--- /dev/null
+++ b/PAR 152/cone_dataset/211.xml	
@@ -0,0 +1,86 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>211.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\211.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>507</width>
+		<height>338</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>169</xmin>
+			<ymin>54</ymin>
+			<xmax>228</xmax>
+			<ymax>131</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>109</xmin>
+			<ymin>51</ymin>
+			<xmax>172</xmax>
+			<ymax>134</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>26</xmin>
+			<ymin>52</ymin>
+			<xmax>91</xmax>
+			<ymax>131</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>280</xmin>
+			<ymin>51</ymin>
+			<xmax>334</xmax>
+			<ymax>127</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>351</xmin>
+			<ymin>52</ymin>
+			<xmax>398</xmax>
+			<ymax>126</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>404</xmin>
+			<ymin>50</ymin>
+			<xmax>452</xmax>
+			<ymax>128</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/212.jpg b/PAR 152/cone_dataset/212.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..d105162a0cc8a16ab51ab74b455dd30e9730dabd
Binary files /dev/null and b/PAR 152/cone_dataset/212.jpg differ
diff --git a/PAR 152/cone_dataset/212.xml b/PAR 152/cone_dataset/212.xml
new file mode 100644
index 0000000000000000000000000000000000000000..0fd6509eec8077a0567167eab9795c40e310e932
--- /dev/null
+++ b/PAR 152/cone_dataset/212.xml	
@@ -0,0 +1,98 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>212.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\212.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>507</width>
+		<height>338</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>49</xmin>
+			<ymin>218</ymin>
+			<xmax>81</xmax>
+			<ymax>262</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>146</xmin>
+			<ymin>218</ymin>
+			<xmax>174</xmax>
+			<ymax>260</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>182</xmin>
+			<ymin>218</ymin>
+			<xmax>210</xmax>
+			<ymax>259</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>210</xmin>
+			<ymin>220</ymin>
+			<xmax>230</xmax>
+			<ymax>257</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>230</xmin>
+			<ymin>218</ymin>
+			<xmax>253</xmax>
+			<ymax>256</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>301</xmin>
+			<ymin>218</ymin>
+			<xmax>326</xmax>
+			<ymax>255</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>443</xmin>
+			<ymin>217</ymin>
+			<xmax>474</xmax>
+			<ymax>250</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/213.jpg b/PAR 152/cone_dataset/213.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..017a02aecbf05187a1eccfe5f5a7e83e087e39a6
Binary files /dev/null and b/PAR 152/cone_dataset/213.jpg differ
diff --git a/PAR 152/cone_dataset/213.xml b/PAR 152/cone_dataset/213.xml
new file mode 100644
index 0000000000000000000000000000000000000000..f8f6777cbe07ce75ecc9d15f7ba2e8d3956e1d14
--- /dev/null
+++ b/PAR 152/cone_dataset/213.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>213.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\213.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>509</width>
+		<height>338</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>209</xmin>
+			<ymin>97</ymin>
+			<xmax>319</xmax>
+			<ymax>304</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/214.jpg b/PAR 152/cone_dataset/214.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..da8d5ba91807901c58b58a5204880cd97b3ddec1
Binary files /dev/null and b/PAR 152/cone_dataset/214.jpg differ
diff --git a/PAR 152/cone_dataset/214.xml b/PAR 152/cone_dataset/214.xml
new file mode 100644
index 0000000000000000000000000000000000000000..b866d7032fc7e4b6c4caba3501ad1c6a49a0552f
--- /dev/null
+++ b/PAR 152/cone_dataset/214.xml	
@@ -0,0 +1,62 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>214.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\214.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>339</width>
+		<height>508</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>52</xmin>
+			<ymin>388</ymin>
+			<xmax>93</xmax>
+			<ymax>495</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>102</xmin>
+			<ymin>365</ymin>
+			<xmax>181</xmax>
+			<ymax>496</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>191</xmin>
+			<ymin>370</ymin>
+			<xmax>248</xmax>
+			<ymax>498</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>240</xmin>
+			<ymin>381</ymin>
+			<xmax>307</xmax>
+			<ymax>501</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/215.jpg b/PAR 152/cone_dataset/215.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..e346361484bab2c896ee157a04add6c205d7f420
Binary files /dev/null and b/PAR 152/cone_dataset/215.jpg differ
diff --git a/PAR 152/cone_dataset/215.xml b/PAR 152/cone_dataset/215.xml
new file mode 100644
index 0000000000000000000000000000000000000000..913b736b02989a9b28e2b72ed9a7f4ca7db9841a
--- /dev/null
+++ b/PAR 152/cone_dataset/215.xml	
@@ -0,0 +1,50 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>215.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\215.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>416</width>
+		<height>416</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>104</xmin>
+			<ymin>319</ymin>
+			<xmax>141</xmax>
+			<ymax>380</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>210</xmin>
+			<ymin>321</ymin>
+			<xmax>246</xmax>
+			<ymax>382</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>294</xmin>
+			<ymin>323</ymin>
+			<xmax>329</xmax>
+			<ymax>381</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/216.jpg b/PAR 152/cone_dataset/216.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..0a10309afd14643d1bd67e0331ca06b059239667
Binary files /dev/null and b/PAR 152/cone_dataset/216.jpg differ
diff --git a/PAR 152/cone_dataset/216.xml b/PAR 152/cone_dataset/216.xml
new file mode 100644
index 0000000000000000000000000000000000000000..68cf18f7ed0afd5dee47d91b1741bc995d7af7f1
--- /dev/null
+++ b/PAR 152/cone_dataset/216.xml	
@@ -0,0 +1,50 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>216.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\216.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>509</width>
+		<height>338</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>143</xmin>
+			<ymin>114</ymin>
+			<xmax>234</xmax>
+			<ymax>273</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>217</xmin>
+			<ymin>152</ymin>
+			<xmax>269</xmax>
+			<ymax>248</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>1</xmin>
+			<ymin>1</ymin>
+			<xmax>114</xmax>
+			<ymax>336</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/217.jpg b/PAR 152/cone_dataset/217.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..c04ddf104a1cee4b33ade230159fec0827b8e79c
Binary files /dev/null and b/PAR 152/cone_dataset/217.jpg differ
diff --git a/PAR 152/cone_dataset/217.xml b/PAR 152/cone_dataset/217.xml
new file mode 100644
index 0000000000000000000000000000000000000000..ee07ea4820f118bb83dc62952249cdc8493c8504
--- /dev/null
+++ b/PAR 152/cone_dataset/217.xml	
@@ -0,0 +1,50 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>217.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\217.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>341</width>
+		<height>506</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>1</xmin>
+			<ymin>219</ymin>
+			<xmax>142</xmax>
+			<ymax>500</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>238</xmin>
+			<ymin>240</ymin>
+			<xmax>341</xmax>
+			<ymax>439</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>304</xmin>
+			<ymin>283</ymin>
+			<xmax>341</xmax>
+			<ymax>391</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/218.jpg b/PAR 152/cone_dataset/218.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..0a1f752d52f18c3aba551b6f7aed5022e2f91066
Binary files /dev/null and b/PAR 152/cone_dataset/218.jpg differ
diff --git a/PAR 152/cone_dataset/218.xml b/PAR 152/cone_dataset/218.xml
new file mode 100644
index 0000000000000000000000000000000000000000..0797498d60e4d2aa78900300a939aa3391aa7907
--- /dev/null
+++ b/PAR 152/cone_dataset/218.xml	
@@ -0,0 +1,86 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>218.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\218.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>508</width>
+		<height>338</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>1</xmin>
+			<ymin>34</ymin>
+			<xmax>132</xmax>
+			<ymax>338</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>140</xmin>
+			<ymin>24</ymin>
+			<xmax>246</xmax>
+			<ymax>262</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>244</xmin>
+			<ymin>22</ymin>
+			<xmax>320</xmax>
+			<ymax>214</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>326</xmin>
+			<ymin>15</ymin>
+			<xmax>385</xmax>
+			<ymax>153</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>392</xmin>
+			<ymin>4</ymin>
+			<xmax>420</xmax>
+			<ymax>97</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>376</xmin>
+			<ymin>7</ymin>
+			<xmax>394</xmax>
+			<ymax>126</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/219.jpg b/PAR 152/cone_dataset/219.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..eed8aac79f979ff1b531ece250df243e668d6cc6
Binary files /dev/null and b/PAR 152/cone_dataset/219.jpg differ
diff --git a/PAR 152/cone_dataset/219.xml b/PAR 152/cone_dataset/219.xml
new file mode 100644
index 0000000000000000000000000000000000000000..9dccd54a75bf67c6e31c2adac5c7fe30f836c03b
--- /dev/null
+++ b/PAR 152/cone_dataset/219.xml	
@@ -0,0 +1,194 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>219.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\219.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>510</width>
+		<height>337</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>308</xmin>
+			<ymin>168</ymin>
+			<xmax>402</xmax>
+			<ymax>327</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>297</xmin>
+			<ymin>120</ymin>
+			<xmax>360</xmax>
+			<ymax>250</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>279</xmin>
+			<ymin>92</ymin>
+			<xmax>326</xmax>
+			<ymax>207</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>369</xmin>
+			<ymin>24</ymin>
+			<xmax>398</xmax>
+			<ymax>77</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>432</xmin>
+			<ymin>20</ymin>
+			<xmax>459</xmax>
+			<ymax>75</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>313</xmin>
+			<ymin>21</ymin>
+			<xmax>341</xmax>
+			<ymax>76</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>152</xmin>
+			<ymin>16</ymin>
+			<xmax>184</xmax>
+			<ymax>64</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>121</xmin>
+			<ymin>15</ymin>
+			<xmax>153</xmax>
+			<ymax>61</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>103</xmin>
+			<ymin>15</ymin>
+			<xmax>121</xmax>
+			<ymax>65</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>72</xmin>
+			<ymin>16</ymin>
+			<xmax>102</xmax>
+			<ymax>62</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>39</xmin>
+			<ymin>17</ymin>
+			<xmax>75</xmax>
+			<ymax>67</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>15</xmin>
+			<ymin>17</ymin>
+			<xmax>51</xmax>
+			<ymax>69</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>260</xmin>
+			<ymin>74</ymin>
+			<xmax>311</xmax>
+			<ymax>173</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>252</xmin>
+			<ymin>61</ymin>
+			<xmax>291</xmax>
+			<ymax>148</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>223</xmin>
+			<ymin>30</ymin>
+			<xmax>244</xmax>
+			<ymax>87</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/22.jpg b/PAR 152/cone_dataset/22.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..391a91991b7b15f907cbe9e240943914afc28de9
Binary files /dev/null and b/PAR 152/cone_dataset/22.jpg differ
diff --git a/PAR 152/cone_dataset/22.xml b/PAR 152/cone_dataset/22.xml
new file mode 100644
index 0000000000000000000000000000000000000000..88bd6c6ca793f1ee76450359ecef7778a8253a82
--- /dev/null
+++ b/PAR 152/cone_dataset/22.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>22.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\22.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>337</width>
+		<height>508</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>3</xmin>
+			<ymin>37</ymin>
+			<xmax>333</xmax>
+			<ymax>478</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/220.jpg b/PAR 152/cone_dataset/220.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..92f76386f69f98b22d2f1b83e534a5e0e307dd56
Binary files /dev/null and b/PAR 152/cone_dataset/220.jpg differ
diff --git a/PAR 152/cone_dataset/220.xml b/PAR 152/cone_dataset/220.xml
new file mode 100644
index 0000000000000000000000000000000000000000..31f460ffd7ee58c79ee058a83f5a724d53a0c34f
--- /dev/null
+++ b/PAR 152/cone_dataset/220.xml	
@@ -0,0 +1,74 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>220.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\220.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>505</width>
+		<height>342</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>202</xmin>
+			<ymin>92</ymin>
+			<xmax>249</xmax>
+			<ymax>178</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>225</xmin>
+			<ymin>182</ymin>
+			<xmax>419</xmax>
+			<ymax>321</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>323</xmin>
+			<ymin>111</ymin>
+			<xmax>370</xmax>
+			<ymax>167</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>265</xmin>
+			<ymin>94</ymin>
+			<xmax>286</xmax>
+			<ymax>138</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>230</xmin>
+			<ymin>109</ymin>
+			<xmax>247</xmax>
+			<ymax>134</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/221.jpg b/PAR 152/cone_dataset/221.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..175b18e445fee83730a8359ea7b74f87f065d70d
Binary files /dev/null and b/PAR 152/cone_dataset/221.jpg differ
diff --git a/PAR 152/cone_dataset/221.xml b/PAR 152/cone_dataset/221.xml
new file mode 100644
index 0000000000000000000000000000000000000000..30214aa533d6a9481bcc6f7df2d2e6921b844579
--- /dev/null
+++ b/PAR 152/cone_dataset/221.xml	
@@ -0,0 +1,170 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>221.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\221.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>507</width>
+		<height>338</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>90</xmin>
+			<ymin>196</ymin>
+			<xmax>118</xmax>
+			<ymax>241</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>118</xmin>
+			<ymin>197</ymin>
+			<xmax>147</xmax>
+			<ymax>241</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>145</xmin>
+			<ymin>197</ymin>
+			<xmax>174</xmax>
+			<ymax>243</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>174</xmin>
+			<ymin>197</ymin>
+			<xmax>201</xmax>
+			<ymax>243</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>200</xmin>
+			<ymin>199</ymin>
+			<xmax>227</xmax>
+			<ymax>244</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>227</xmin>
+			<ymin>198</ymin>
+			<xmax>254</xmax>
+			<ymax>242</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>255</xmin>
+			<ymin>197</ymin>
+			<xmax>282</xmax>
+			<ymax>243</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>282</xmin>
+			<ymin>198</ymin>
+			<xmax>308</xmax>
+			<ymax>243</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>309</xmin>
+			<ymin>198</ymin>
+			<xmax>337</xmax>
+			<ymax>244</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>338</xmin>
+			<ymin>196</ymin>
+			<xmax>362</xmax>
+			<ymax>247</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>362</xmin>
+			<ymin>197</ymin>
+			<xmax>391</xmax>
+			<ymax>246</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>392</xmin>
+			<ymin>198</ymin>
+			<xmax>417</xmax>
+			<ymax>244</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>417</xmin>
+			<ymin>195</ymin>
+			<xmax>445</xmax>
+			<ymax>244</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/222.jpg b/PAR 152/cone_dataset/222.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..0f7204ae92e89a5d2a0eedbf3722ff950d4dc382
Binary files /dev/null and b/PAR 152/cone_dataset/222.jpg differ
diff --git a/PAR 152/cone_dataset/222.xml b/PAR 152/cone_dataset/222.xml
new file mode 100644
index 0000000000000000000000000000000000000000..0d82b5c3ed1365cd03f0ee7e759f9710b873d871
--- /dev/null
+++ b/PAR 152/cone_dataset/222.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>222.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\222.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>339</width>
+		<height>509</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>1</xmin>
+			<ymin>140</ymin>
+			<xmax>179</xmax>
+			<ymax>497</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/223.jpg b/PAR 152/cone_dataset/223.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..6d0eb5c7ccf6bd5c27b68b9bc84c341dfbc3f791
Binary files /dev/null and b/PAR 152/cone_dataset/223.jpg differ
diff --git a/PAR 152/cone_dataset/223.xml b/PAR 152/cone_dataset/223.xml
new file mode 100644
index 0000000000000000000000000000000000000000..25a28926da320788a8e1e7645fa5080192ec6276
--- /dev/null
+++ b/PAR 152/cone_dataset/223.xml	
@@ -0,0 +1,38 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>223.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\223.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>507</width>
+		<height>338</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>99</xmin>
+			<ymin>60</ymin>
+			<xmax>194</xmax>
+			<ymax>234</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>279</xmin>
+			<ymin>51</ymin>
+			<xmax>398</xmax>
+			<ymax>303</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/224.jpg b/PAR 152/cone_dataset/224.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..b5dc95b4bbff987d767bcf1ea17aa0d6f2b9de73
Binary files /dev/null and b/PAR 152/cone_dataset/224.jpg differ
diff --git a/PAR 152/cone_dataset/224.xml b/PAR 152/cone_dataset/224.xml
new file mode 100644
index 0000000000000000000000000000000000000000..2b117f04131ab5d1bb6a790b3a85f1977c305ef4
--- /dev/null
+++ b/PAR 152/cone_dataset/224.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>224.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\224.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>338</width>
+		<height>507</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>135</xmin>
+			<ymin>182</ymin>
+			<xmax>266</xmax>
+			<ymax>462</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/225.jpg b/PAR 152/cone_dataset/225.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..edf9363b9df8bd614dbe3af016fa2a1404fd7d88
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diff --git a/PAR 152/cone_dataset/225.xml b/PAR 152/cone_dataset/225.xml
new file mode 100644
index 0000000000000000000000000000000000000000..57289b095fc96c0af8251229bd3fdaa9355cb484
--- /dev/null
+++ b/PAR 152/cone_dataset/225.xml	
@@ -0,0 +1,38 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>225.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\225.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>479</width>
+		<height>359</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>180</xmin>
+			<ymin>139</ymin>
+			<xmax>236</xmax>
+			<ymax>234</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>252</xmin>
+			<ymin>126</ymin>
+			<xmax>304</xmax>
+			<ymax>213</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/226.jpg b/PAR 152/cone_dataset/226.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..87717e2c0d874eaf9d0d8bebbd3fb497a5238f09
Binary files /dev/null and b/PAR 152/cone_dataset/226.jpg differ
diff --git a/PAR 152/cone_dataset/226.xml b/PAR 152/cone_dataset/226.xml
new file mode 100644
index 0000000000000000000000000000000000000000..7b7abaa075930e80a3de28d2ee554890feeabf1a
--- /dev/null
+++ b/PAR 152/cone_dataset/226.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>226.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\226.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>678</width>
+		<height>1024</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>123</xmin>
+			<ymin>145</ymin>
+			<xmax>573</xmax>
+			<ymax>970</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/227.jpg b/PAR 152/cone_dataset/227.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..f181fcbdf0cef0201aaa506be1a38355c0659867
Binary files /dev/null and b/PAR 152/cone_dataset/227.jpg differ
diff --git a/PAR 152/cone_dataset/227.xml b/PAR 152/cone_dataset/227.xml
new file mode 100644
index 0000000000000000000000000000000000000000..49b939d640ee4152d439b8620ce3deefac6bbd0a
--- /dev/null
+++ b/PAR 152/cone_dataset/227.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>227.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\227.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>338</width>
+		<height>511</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>16</xmin>
+			<ymin>18</ymin>
+			<xmax>280</xmax>
+			<ymax>499</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/228.jpg b/PAR 152/cone_dataset/228.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..d9128e6eada1f688a5b95cc055d340dbae521dc2
Binary files /dev/null and b/PAR 152/cone_dataset/228.jpg differ
diff --git a/PAR 152/cone_dataset/228.xml b/PAR 152/cone_dataset/228.xml
new file mode 100644
index 0000000000000000000000000000000000000000..9adab7e03408dc312f79055fca43f9a67b25115b
--- /dev/null
+++ b/PAR 152/cone_dataset/228.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>228.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\228.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>170</width>
+		<height>170</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>85</xmin>
+			<ymin>16</ymin>
+			<xmax>159</xmax>
+			<ymax>147</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/229.jpg b/PAR 152/cone_dataset/229.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..7a1a8dac1ed0e2cf3ba26e6f814a196a2a6a848d
Binary files /dev/null and b/PAR 152/cone_dataset/229.jpg differ
diff --git a/PAR 152/cone_dataset/229.xml b/PAR 152/cone_dataset/229.xml
new file mode 100644
index 0000000000000000000000000000000000000000..944b54f47c74890477224450f1aff7127ca3b421
--- /dev/null
+++ b/PAR 152/cone_dataset/229.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>229.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\229.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>416</width>
+		<height>416</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>214</xmin>
+			<ymin>38</ymin>
+			<xmax>387</xmax>
+			<ymax>363</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/23.jpg b/PAR 152/cone_dataset/23.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..1c6090a4c68c0c9980f44f746d4476e0067b0419
Binary files /dev/null and b/PAR 152/cone_dataset/23.jpg differ
diff --git a/PAR 152/cone_dataset/23.xml b/PAR 152/cone_dataset/23.xml
new file mode 100644
index 0000000000000000000000000000000000000000..85ba55bbaf08d5df33644dc84a711c8e5b1b2a07
--- /dev/null
+++ b/PAR 152/cone_dataset/23.xml	
@@ -0,0 +1,38 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>23.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\23.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>478</width>
+		<height>359</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>16</xmin>
+			<ymin>29</ymin>
+			<xmax>261</xmax>
+			<ymax>310</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>242</xmin>
+			<ymin>76</ymin>
+			<xmax>439</xmax>
+			<ymax>271</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/230.jpg b/PAR 152/cone_dataset/230.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..c94a5d54e91d8d3c16656a67e8ca24f76118ab45
Binary files /dev/null and b/PAR 152/cone_dataset/230.jpg differ
diff --git a/PAR 152/cone_dataset/230.xml b/PAR 152/cone_dataset/230.xml
new file mode 100644
index 0000000000000000000000000000000000000000..d056eb59e926bbd8f90485ee74a71c63d09617d8
--- /dev/null
+++ b/PAR 152/cone_dataset/230.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>230.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\230.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>356</width>
+		<height>481</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>101</xmin>
+			<ymin>88</ymin>
+			<xmax>244</xmax>
+			<ymax>318</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/231.jpg b/PAR 152/cone_dataset/231.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..258d884e424fe6334c7a7921ada3b3f20d8899f6
Binary files /dev/null and b/PAR 152/cone_dataset/231.jpg differ
diff --git a/PAR 152/cone_dataset/231.xml b/PAR 152/cone_dataset/231.xml
new file mode 100644
index 0000000000000000000000000000000000000000..d3a69a02992ba4901e78e22665506209ddaad6bc
--- /dev/null
+++ b/PAR 152/cone_dataset/231.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>231.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\231.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>338</width>
+		<height>511</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>97</xmin>
+			<ymin>87</ymin>
+			<xmax>234</xmax>
+			<ymax>335</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/232.jpg b/PAR 152/cone_dataset/232.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..bca15da6737f9d93e710a83704acdf6a6e3d54c7
Binary files /dev/null and b/PAR 152/cone_dataset/232.jpg differ
diff --git a/PAR 152/cone_dataset/232.xml b/PAR 152/cone_dataset/232.xml
new file mode 100644
index 0000000000000000000000000000000000000000..06d42a1305233062b9f57e54278c63873a1e555b
--- /dev/null
+++ b/PAR 152/cone_dataset/232.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>232.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\232.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>416</width>
+		<height>416</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>24</xmin>
+			<ymin>51</ymin>
+			<xmax>223</xmax>
+			<ymax>387</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/233.jpg b/PAR 152/cone_dataset/233.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..2b2f24aa2c7f3101f2dd22723f898354aaae5036
Binary files /dev/null and b/PAR 152/cone_dataset/233.jpg differ
diff --git a/PAR 152/cone_dataset/233.xml b/PAR 152/cone_dataset/233.xml
new file mode 100644
index 0000000000000000000000000000000000000000..d6c3bc6fee80becebfe589cf671dd066b673aa4f
--- /dev/null
+++ b/PAR 152/cone_dataset/233.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>233.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\233.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>482</width>
+		<height>359</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>375</xmin>
+			<ymin>1</ymin>
+			<xmax>479</xmax>
+			<ymax>210</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/234.jpg b/PAR 152/cone_dataset/234.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..fbb18efe4f01563d610d8c6b2dfcacfcb37bc59a
Binary files /dev/null and b/PAR 152/cone_dataset/234.jpg differ
diff --git a/PAR 152/cone_dataset/234.xml b/PAR 152/cone_dataset/234.xml
new file mode 100644
index 0000000000000000000000000000000000000000..a6a4e2ddc7924bbeacf6ab90ac661a1dbb83e61d
--- /dev/null
+++ b/PAR 152/cone_dataset/234.xml	
@@ -0,0 +1,38 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>234.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\234.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>371</width>
+		<height>464</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>6</xmin>
+			<ymin>155</ymin>
+			<xmax>173</xmax>
+			<ymax>456</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>71</xmin>
+			<ymin>23</ymin>
+			<xmax>224</xmax>
+			<ymax>371</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/235.jpg b/PAR 152/cone_dataset/235.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..1cb3cea7568f7b51ee3a66e7b02109ef25f69ad4
Binary files /dev/null and b/PAR 152/cone_dataset/235.jpg differ
diff --git a/PAR 152/cone_dataset/235.xml b/PAR 152/cone_dataset/235.xml
new file mode 100644
index 0000000000000000000000000000000000000000..39f8da132e732cd8868bfb01c3a222fafeb16cbf
--- /dev/null
+++ b/PAR 152/cone_dataset/235.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>235.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\235.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>509</width>
+		<height>339</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>271</xmin>
+			<ymin>42</ymin>
+			<xmax>386</xmax>
+			<ymax>206</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/236.jpg b/PAR 152/cone_dataset/236.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..e9bcf0e2841f4b0ccb804ff8a9b9f9cc473eaa34
Binary files /dev/null and b/PAR 152/cone_dataset/236.jpg differ
diff --git a/PAR 152/cone_dataset/236.xml b/PAR 152/cone_dataset/236.xml
new file mode 100644
index 0000000000000000000000000000000000000000..26ba8bc91a84826611b19c7018710a94c2127497
--- /dev/null
+++ b/PAR 152/cone_dataset/236.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>236.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\236.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>384</width>
+		<height>450</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>25</xmin>
+			<ymin>80</ymin>
+			<xmax>225</xmax>
+			<ymax>418</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/237.jpg b/PAR 152/cone_dataset/237.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..be727126b76d4890224030906be199e9af38e592
Binary files /dev/null and b/PAR 152/cone_dataset/237.jpg differ
diff --git a/PAR 152/cone_dataset/237.xml b/PAR 152/cone_dataset/237.xml
new file mode 100644
index 0000000000000000000000000000000000000000..add17552a010f76d44ca23368e5bf3e25e92b78a
--- /dev/null
+++ b/PAR 152/cone_dataset/237.xml	
@@ -0,0 +1,62 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>237.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\237.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>357</width>
+		<height>479</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>39</xmin>
+			<ymin>317</ymin>
+			<xmax>122</xmax>
+			<ymax>427</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>30</xmin>
+			<ymin>288</ymin>
+			<xmax>72</xmax>
+			<ymax>361</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>17</xmin>
+			<ymin>278</ymin>
+			<xmax>46</xmax>
+			<ymax>332</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>7</xmin>
+			<ymin>266</ymin>
+			<xmax>26</xmax>
+			<ymax>298</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/238.jpg b/PAR 152/cone_dataset/238.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..1972fe77d33248d51ce700c2f7492aa243efc853
Binary files /dev/null and b/PAR 152/cone_dataset/238.jpg differ
diff --git a/PAR 152/cone_dataset/238.xml b/PAR 152/cone_dataset/238.xml
new file mode 100644
index 0000000000000000000000000000000000000000..093e95bfbe254312e2fdbda602fd8b7fbdea7c92
--- /dev/null
+++ b/PAR 152/cone_dataset/238.xml	
@@ -0,0 +1,74 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>238.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\238.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>478</width>
+		<height>359</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>80</xmin>
+			<ymin>35</ymin>
+			<xmax>182</xmax>
+			<ymax>209</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>152</xmin>
+			<ymin>14</ymin>
+			<xmax>238</xmax>
+			<ymax>161</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>154</xmin>
+			<ymin>84</ymin>
+			<xmax>259</xmax>
+			<ymax>281</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>269</xmin>
+			<ymin>72</ymin>
+			<xmax>360</xmax>
+			<ymax>256</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>249</xmin>
+			<ymin>26</ymin>
+			<xmax>320</xmax>
+			<ymax>174</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/239.jpg b/PAR 152/cone_dataset/239.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..a3dc1058cc157923dd114cbb6b3fb37f31fab923
Binary files /dev/null and b/PAR 152/cone_dataset/239.jpg differ
diff --git a/PAR 152/cone_dataset/239.xml b/PAR 152/cone_dataset/239.xml
new file mode 100644
index 0000000000000000000000000000000000000000..c05abbfabfad744e3b77906448753800812b1f86
--- /dev/null
+++ b/PAR 152/cone_dataset/239.xml	
@@ -0,0 +1,38 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>239.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\239.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>509</width>
+		<height>339</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>172</xmin>
+			<ymin>54</ymin>
+			<xmax>225</xmax>
+			<ymax>147</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>340</xmin>
+			<ymin>53</ymin>
+			<xmax>479</xmax>
+			<ymax>326</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/24.jpg b/PAR 152/cone_dataset/24.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..5130b019100de3152f3be049a2fc5cd2379ce384
Binary files /dev/null and b/PAR 152/cone_dataset/24.jpg differ
diff --git a/PAR 152/cone_dataset/24.xml b/PAR 152/cone_dataset/24.xml
new file mode 100644
index 0000000000000000000000000000000000000000..ebbeef64420c6fc0164f424155052c4bf7820636
--- /dev/null
+++ b/PAR 152/cone_dataset/24.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>24.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\24.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>339</width>
+		<height>509</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>34</xmin>
+			<ymin>65</ymin>
+			<xmax>319</xmax>
+			<ymax>474</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/240.jpg b/PAR 152/cone_dataset/240.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..ac4d431210e725dd960957978cd7692581c29574
Binary files /dev/null and b/PAR 152/cone_dataset/240.jpg differ
diff --git a/PAR 152/cone_dataset/240.xml b/PAR 152/cone_dataset/240.xml
new file mode 100644
index 0000000000000000000000000000000000000000..3b707f184b432f9c9f8fbb8030ed52ded558b84c
--- /dev/null
+++ b/PAR 152/cone_dataset/240.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>240.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\240.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>507</width>
+		<height>339</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>117</xmin>
+			<ymin>71</ymin>
+			<xmax>187</xmax>
+			<ymax>185</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/241.jpg b/PAR 152/cone_dataset/241.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..4e037b74457a8fb282a17d57a6d1a899174037fd
Binary files /dev/null and b/PAR 152/cone_dataset/241.jpg differ
diff --git a/PAR 152/cone_dataset/241.xml b/PAR 152/cone_dataset/241.xml
new file mode 100644
index 0000000000000000000000000000000000000000..3b3992d4cb4d1bbd644f4999d8f51037c63ba0c5
--- /dev/null
+++ b/PAR 152/cone_dataset/241.xml	
@@ -0,0 +1,38 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>241.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\241.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>509</width>
+		<height>339</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>75</xmin>
+			<ymin>44</ymin>
+			<xmax>138</xmax>
+			<ymax>133</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>279</xmin>
+			<ymin>162</ymin>
+			<xmax>357</xmax>
+			<ymax>282</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/242.jpg b/PAR 152/cone_dataset/242.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..b040bf209944dcc3185df1fbf1103dc3783b20ca
Binary files /dev/null and b/PAR 152/cone_dataset/242.jpg differ
diff --git a/PAR 152/cone_dataset/242.xml b/PAR 152/cone_dataset/242.xml
new file mode 100644
index 0000000000000000000000000000000000000000..7c184931388544e153ce3c688a22bdc2ad1a65c1
--- /dev/null
+++ b/PAR 152/cone_dataset/242.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>242.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\242.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>576</width>
+		<height>1024</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>207</xmin>
+			<ymin>34</ymin>
+			<xmax>552</xmax>
+			<ymax>668</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/243.jpg b/PAR 152/cone_dataset/243.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..f09beff05baf9c3e207e621bd747b66a8481727b
Binary files /dev/null and b/PAR 152/cone_dataset/243.jpg differ
diff --git a/PAR 152/cone_dataset/243.xml b/PAR 152/cone_dataset/243.xml
new file mode 100644
index 0000000000000000000000000000000000000000..004ad9ae2dab2731f3f5fae99b13b907f109fe05
--- /dev/null
+++ b/PAR 152/cone_dataset/243.xml	
@@ -0,0 +1,74 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>243.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\243.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>507</width>
+		<height>338</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>209</xmin>
+			<ymin>30</ymin>
+			<xmax>315</xmax>
+			<ymax>226</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>25</xmin>
+			<ymin>1</ymin>
+			<xmax>113</xmax>
+			<ymax>125</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>83</xmin>
+			<ymin>1</ymin>
+			<xmax>163</xmax>
+			<ymax>81</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>262</xmin>
+			<ymin>114</ymin>
+			<xmax>410</xmax>
+			<ymax>338</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>270</xmin>
+			<ymin>1</ymin>
+			<xmax>332</xmax>
+			<ymax>124</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/244.jpg b/PAR 152/cone_dataset/244.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..dbdbb15c4be11c3ed9d44495e2c18d667f94a986
Binary files /dev/null and b/PAR 152/cone_dataset/244.jpg differ
diff --git a/PAR 152/cone_dataset/244.xml b/PAR 152/cone_dataset/244.xml
new file mode 100644
index 0000000000000000000000000000000000000000..cc9009d4d839ea3e4a11cd9f476f2560285cd103
--- /dev/null
+++ b/PAR 152/cone_dataset/244.xml	
@@ -0,0 +1,122 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>244.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\244.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>338</width>
+		<height>507</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>272</xmin>
+			<ymin>163</ymin>
+			<xmax>338</xmax>
+			<ymax>272</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>212</xmin>
+			<ymin>175</ymin>
+			<xmax>271</xmax>
+			<ymax>264</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>169</xmin>
+			<ymin>181</ymin>
+			<xmax>223</xmax>
+			<ymax>259</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>137</xmin>
+			<ymin>189</ymin>
+			<xmax>183</xmax>
+			<ymax>258</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>115</xmin>
+			<ymin>194</ymin>
+			<xmax>145</xmax>
+			<ymax>256</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>94</xmin>
+			<ymin>198</ymin>
+			<xmax>124</xmax>
+			<ymax>253</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>80</xmin>
+			<ymin>200</ymin>
+			<xmax>104</xmax>
+			<ymax>252</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>68</xmin>
+			<ymin>203</ymin>
+			<xmax>89</xmax>
+			<ymax>250</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>58</xmin>
+			<ymin>206</ymin>
+			<xmax>73</xmax>
+			<ymax>247</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/245.jpg b/PAR 152/cone_dataset/245.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..6c4cd50a01627e39b03b0607352f62c5c75b3903
Binary files /dev/null and b/PAR 152/cone_dataset/245.jpg differ
diff --git a/PAR 152/cone_dataset/245.xml b/PAR 152/cone_dataset/245.xml
new file mode 100644
index 0000000000000000000000000000000000000000..1ea01ddec8debf2d735a3f18ea90008c9750d161
--- /dev/null
+++ b/PAR 152/cone_dataset/245.xml	
@@ -0,0 +1,98 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>245.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\245.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>338</width>
+		<height>507</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>1</xmin>
+			<ymin>275</ymin>
+			<xmax>68</xmax>
+			<ymax>362</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>72</xmin>
+			<ymin>286</ymin>
+			<xmax>124</xmax>
+			<ymax>358</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>123</xmin>
+			<ymin>294</ymin>
+			<xmax>164</xmax>
+			<ymax>354</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>159</xmin>
+			<ymin>299</ymin>
+			<xmax>193</xmax>
+			<ymax>354</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>190</xmin>
+			<ymin>302</ymin>
+			<xmax>215</xmax>
+			<ymax>354</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>208</xmin>
+			<ymin>306</ymin>
+			<xmax>232</xmax>
+			<ymax>354</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>227</xmin>
+			<ymin>312</ymin>
+			<xmax>249</xmax>
+			<ymax>350</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/246.jpg b/PAR 152/cone_dataset/246.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..001070d978da9587d07f2dc68a80968adc611569
Binary files /dev/null and b/PAR 152/cone_dataset/246.jpg differ
diff --git a/PAR 152/cone_dataset/246.xml b/PAR 152/cone_dataset/246.xml
new file mode 100644
index 0000000000000000000000000000000000000000..f9cca2da0d6f09bbe1e46a9fdea5d086b8d00fa9
--- /dev/null
+++ b/PAR 152/cone_dataset/246.xml	
@@ -0,0 +1,86 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>246.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\246.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>338</width>
+		<height>507</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>1</xmin>
+			<ymin>199</ymin>
+			<xmax>111</xmax>
+			<ymax>353</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>87</xmin>
+			<ymin>238</ymin>
+			<xmax>158</xmax>
+			<ymax>346</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>138</xmin>
+			<ymin>252</ymin>
+			<xmax>184</xmax>
+			<ymax>340</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>162</xmin>
+			<ymin>267</ymin>
+			<xmax>204</xmax>
+			<ymax>338</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>187</xmin>
+			<ymin>275</ymin>
+			<xmax>211</xmax>
+			<ymax>337</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>210</xmin>
+			<ymin>287</ymin>
+			<xmax>225</xmax>
+			<ymax>335</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/247.jpg b/PAR 152/cone_dataset/247.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..0c940cbe7c5cd469f3fbb73142fdd486b47526db
Binary files /dev/null and b/PAR 152/cone_dataset/247.jpg differ
diff --git a/PAR 152/cone_dataset/247.xml b/PAR 152/cone_dataset/247.xml
new file mode 100644
index 0000000000000000000000000000000000000000..151d36129f29ad732edc32b3a72394f32b0d8a39
--- /dev/null
+++ b/PAR 152/cone_dataset/247.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>247.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\247.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>509</width>
+		<height>339</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>197</xmin>
+			<ymin>101</ymin>
+			<xmax>344</xmax>
+			<ymax>293</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/248.jpg b/PAR 152/cone_dataset/248.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..95e4cec4f42e35180f89a51ccdc4c6a75e37db54
Binary files /dev/null and b/PAR 152/cone_dataset/248.jpg differ
diff --git a/PAR 152/cone_dataset/248.xml b/PAR 152/cone_dataset/248.xml
new file mode 100644
index 0000000000000000000000000000000000000000..8a568d3149339cd72a913968fbd12a3c19f04568
--- /dev/null
+++ b/PAR 152/cone_dataset/248.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>248.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\248.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>509</width>
+		<height>339</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>296</xmin>
+			<ymin>184</ymin>
+			<xmax>408</xmax>
+			<ymax>319</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/249.jpg b/PAR 152/cone_dataset/249.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..6768d8dc27aca8260bdfc85b2e48c4e4d238df21
Binary files /dev/null and b/PAR 152/cone_dataset/249.jpg differ
diff --git a/PAR 152/cone_dataset/249.xml b/PAR 152/cone_dataset/249.xml
new file mode 100644
index 0000000000000000000000000000000000000000..a48aa2ca13482356050272461e63b309381ed7a8
--- /dev/null
+++ b/PAR 152/cone_dataset/249.xml	
@@ -0,0 +1,134 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>249.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\249.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>507</width>
+		<height>338</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>130</xmin>
+			<ymin>259</ymin>
+			<xmax>171</xmax>
+			<ymax>321</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>165</xmin>
+			<ymin>232</ymin>
+			<xmax>199</xmax>
+			<ymax>282</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>189</xmin>
+			<ymin>213</ymin>
+			<xmax>216</xmax>
+			<ymax>255</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>206</xmin>
+			<ymin>200</ymin>
+			<xmax>230</xmax>
+			<ymax>237</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>218</xmin>
+			<ymin>190</ymin>
+			<xmax>239</xmax>
+			<ymax>221</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>230</xmin>
+			<ymin>183</ymin>
+			<xmax>246</xmax>
+			<ymax>211</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>237</xmin>
+			<ymin>178</ymin>
+			<xmax>254</xmax>
+			<ymax>201</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>243</xmin>
+			<ymin>172</ymin>
+			<xmax>259</xmax>
+			<ymax>193</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>256</xmin>
+			<ymin>162</ymin>
+			<xmax>266</xmax>
+			<ymax>181</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>85</xmin>
+			<ymin>306</ymin>
+			<xmax>113</xmax>
+			<ymax>338</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/25.jpg b/PAR 152/cone_dataset/25.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..5b1541eb462eeb33b26a741d5097004500d5f277
Binary files /dev/null and b/PAR 152/cone_dataset/25.jpg differ
diff --git a/PAR 152/cone_dataset/25.xml b/PAR 152/cone_dataset/25.xml
new file mode 100644
index 0000000000000000000000000000000000000000..ff99b86d07e94aa8ae0bb50c3c99074a2773dbc1
--- /dev/null
+++ b/PAR 152/cone_dataset/25.xml	
@@ -0,0 +1,38 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>25.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\25.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>1024</width>
+		<height>989</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>106</xmin>
+			<ymin>107</ymin>
+			<xmax>640</xmax>
+			<ymax>913</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>470</xmin>
+			<ymin>281</ymin>
+			<xmax>936</xmax>
+			<ymax>657</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/250.jpg b/PAR 152/cone_dataset/250.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..3fc522811442ecdd3fc27f9c956738ce60c76cdf
Binary files /dev/null and b/PAR 152/cone_dataset/250.jpg differ
diff --git a/PAR 152/cone_dataset/250.xml b/PAR 152/cone_dataset/250.xml
new file mode 100644
index 0000000000000000000000000000000000000000..4db7a2b25d00279a3df16b09a64b8228049699c5
--- /dev/null
+++ b/PAR 152/cone_dataset/250.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>250.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\250.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>533</width>
+		<height>651</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>172</xmin>
+			<ymin>201</ymin>
+			<xmax>457</xmax>
+			<ymax>594</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/251.jpg b/PAR 152/cone_dataset/251.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..9363b12557d4359ace4b4d1a43b18e53bb117846
Binary files /dev/null and b/PAR 152/cone_dataset/251.jpg differ
diff --git a/PAR 152/cone_dataset/251.xml b/PAR 152/cone_dataset/251.xml
new file mode 100644
index 0000000000000000000000000000000000000000..7fcc8409c4e90d8ad740eb6ce5fa1cdac781c6a1
--- /dev/null
+++ b/PAR 152/cone_dataset/251.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>251.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\251.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>353</width>
+		<height>485</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>56</xmin>
+			<ymin>3</ymin>
+			<xmax>292</xmax>
+			<ymax>445</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/252.jpg b/PAR 152/cone_dataset/252.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..b8c048bdf1ea5c25b21ab731284d1c2be20f62d6
Binary files /dev/null and b/PAR 152/cone_dataset/252.jpg differ
diff --git a/PAR 152/cone_dataset/252.xml b/PAR 152/cone_dataset/252.xml
new file mode 100644
index 0000000000000000000000000000000000000000..dadb13d259b16ff2a085bd66396e33e467727e9b
--- /dev/null
+++ b/PAR 152/cone_dataset/252.xml	
@@ -0,0 +1,122 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>252.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\252.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>375</width>
+		<height>458</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>181</xmin>
+			<ymin>298</ymin>
+			<xmax>231</xmax>
+			<ymax>389</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>205</xmin>
+			<ymin>242</ymin>
+			<xmax>243</xmax>
+			<ymax>316</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>247</xmin>
+			<ymin>205</ymin>
+			<xmax>279</xmax>
+			<ymax>266</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>258</xmin>
+			<ymin>172</ymin>
+			<xmax>286</xmax>
+			<ymax>219</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>244</xmin>
+			<ymin>147</ymin>
+			<xmax>265</xmax>
+			<ymax>182</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>198</xmin>
+			<ymin>128</ymin>
+			<xmax>222</xmax>
+			<ymax>158</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>145</xmin>
+			<ymin>129</ymin>
+			<xmax>162</xmax>
+			<ymax>162</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>100</xmin>
+			<ymin>152</ymin>
+			<xmax>123</xmax>
+			<ymax>194</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>84</xmin>
+			<ymin>180</ymin>
+			<xmax>112</xmax>
+			<ymax>234</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/253.jpg b/PAR 152/cone_dataset/253.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..3a349575a3217e03605fd0c6c71916210011cc43
Binary files /dev/null and b/PAR 152/cone_dataset/253.jpg differ
diff --git a/PAR 152/cone_dataset/253.xml b/PAR 152/cone_dataset/253.xml
new file mode 100644
index 0000000000000000000000000000000000000000..16c933e2bf71d555361d697b4d5ce3cdb81222c8
--- /dev/null
+++ b/PAR 152/cone_dataset/253.xml	
@@ -0,0 +1,38 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>253.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\253.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>338</width>
+		<height>507</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>150</xmin>
+			<ymin>218</ymin>
+			<xmax>262</xmax>
+			<ymax>464</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>273</xmin>
+			<ymin>243</ymin>
+			<xmax>338</xmax>
+			<ymax>507</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/254.jpg b/PAR 152/cone_dataset/254.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..54b18ae28cde2e3d092d00eca0c2d982eb2bac11
Binary files /dev/null and b/PAR 152/cone_dataset/254.jpg differ
diff --git a/PAR 152/cone_dataset/254.xml b/PAR 152/cone_dataset/254.xml
new file mode 100644
index 0000000000000000000000000000000000000000..42224d1fb8afba80f3b646ea6bc56f12a356caad
--- /dev/null
+++ b/PAR 152/cone_dataset/254.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>254.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\254.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>478</width>
+		<height>358</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>29</xmin>
+			<ymin>108</ymin>
+			<xmax>423</xmax>
+			<ymax>309</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/255.jpg b/PAR 152/cone_dataset/255.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..9f8ee6d4c11cb00f8801d6fecf48e58e470e7c86
Binary files /dev/null and b/PAR 152/cone_dataset/255.jpg differ
diff --git a/PAR 152/cone_dataset/255.xml b/PAR 152/cone_dataset/255.xml
new file mode 100644
index 0000000000000000000000000000000000000000..e39b53d456ba8157efca896ce47c63537b17c836
--- /dev/null
+++ b/PAR 152/cone_dataset/255.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>255.jpg</filename>
+	<path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\255.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>339</width>
+		<height>506</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>68</xmin>
+			<ymin>52</ymin>
+			<xmax>299</xmax>
+			<ymax>467</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/26.jpg b/PAR 152/cone_dataset/26.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..e542081c3d472297955a5e3a74513409c2494ded
Binary files /dev/null and b/PAR 152/cone_dataset/26.jpg differ
diff --git a/PAR 152/cone_dataset/26.xml b/PAR 152/cone_dataset/26.xml
new file mode 100644
index 0000000000000000000000000000000000000000..21e4685b0b0c520c8a9b3c399189003e0b003557
--- /dev/null
+++ b/PAR 152/cone_dataset/26.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>26.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\26.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>686</width>
+		<height>1024</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>120</xmin>
+			<ymin>115</ymin>
+			<xmax>555</xmax>
+			<ymax>963</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/27.jpg b/PAR 152/cone_dataset/27.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..f9b98b3c5bfa0f5bbee41a12a04b8ffa08892dbc
Binary files /dev/null and b/PAR 152/cone_dataset/27.jpg differ
diff --git a/PAR 152/cone_dataset/27.xml b/PAR 152/cone_dataset/27.xml
new file mode 100644
index 0000000000000000000000000000000000000000..269536d45d00144d322bc0772181ce30b54d0ebc
--- /dev/null
+++ b/PAR 152/cone_dataset/27.xml	
@@ -0,0 +1,86 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>27.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\27.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>602</width>
+		<height>1024</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>177</xmin>
+			<ymin>125</ymin>
+			<xmax>421</xmax>
+			<ymax>697</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>337</xmin>
+			<ymin>269</ymin>
+			<xmax>602</xmax>
+			<ymax>1023</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>168</xmin>
+			<ymin>105</ymin>
+			<xmax>316</xmax>
+			<ymax>464</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>166</xmin>
+			<ymin>115</ymin>
+			<xmax>247</xmax>
+			<ymax>343</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>48</xmin>
+			<ymin>74</ymin>
+			<xmax>124</xmax>
+			<ymax>207</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>1</xmin>
+			<ymin>73</ymin>
+			<xmax>45</xmax>
+			<ymax>179</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/28.jpg b/PAR 152/cone_dataset/28.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..fa2c3f401141fd670eada8faf5d31b33e5c77e8c
Binary files /dev/null and b/PAR 152/cone_dataset/28.jpg differ
diff --git a/PAR 152/cone_dataset/28.xml b/PAR 152/cone_dataset/28.xml
new file mode 100644
index 0000000000000000000000000000000000000000..cbd1807eb72ba55ed8fdca85ea4c3ac21ee60edc
--- /dev/null
+++ b/PAR 152/cone_dataset/28.xml	
@@ -0,0 +1,86 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>28.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\28.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>1024</width>
+		<height>658</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>1</xmin>
+			<ymin>46</ymin>
+			<xmax>246</xmax>
+			<ymax>655</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>1</xmin>
+			<ymin>42</ymin>
+			<xmax>265</xmax>
+			<ymax>562</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>431</xmin>
+			<ymin>225</ymin>
+			<xmax>537</xmax>
+			<ymax>393</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>652</xmin>
+			<ymin>244</ymin>
+			<xmax>737</xmax>
+			<ymax>371</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>813</xmin>
+			<ymin>260</ymin>
+			<xmax>894</xmax>
+			<ymax>362</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>316</xmin>
+			<ymin>197</ymin>
+			<xmax>389</xmax>
+			<ymax>432</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/29.jpg b/PAR 152/cone_dataset/29.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..7afafddad9203c7f2cd1acd4777edb8161939207
Binary files /dev/null and b/PAR 152/cone_dataset/29.jpg differ
diff --git a/PAR 152/cone_dataset/29.xml b/PAR 152/cone_dataset/29.xml
new file mode 100644
index 0000000000000000000000000000000000000000..a87ff5cd2153626ebc41d319f3c832a7b08269ee
--- /dev/null
+++ b/PAR 152/cone_dataset/29.xml	
@@ -0,0 +1,74 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>29.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\29.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>1024</width>
+		<height>658</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>390</xmin>
+			<ymin>259</ymin>
+			<xmax>632</xmax>
+			<ymax>417</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>806</xmin>
+			<ymin>256</ymin>
+			<xmax>895</xmax>
+			<ymax>361</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>1</xmin>
+			<ymin>41</ymin>
+			<xmax>261</xmax>
+			<ymax>561</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>250</xmin>
+			<ymin>181</ymin>
+			<xmax>393</xmax>
+			<ymax>430</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>711</xmin>
+			<ymin>251</ymin>
+			<xmax>788</xmax>
+			<ymax>365</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/3.jpg b/PAR 152/cone_dataset/3.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..899a0b2d4fe10b46d0bba0fbdf7ee9743199e52c
Binary files /dev/null and b/PAR 152/cone_dataset/3.jpg differ
diff --git a/PAR 152/cone_dataset/3.xml b/PAR 152/cone_dataset/3.xml
new file mode 100644
index 0000000000000000000000000000000000000000..4c93fbceab56b63b9c500003be55dcd5179ce1ce
--- /dev/null
+++ b/PAR 152/cone_dataset/3.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>3.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\3.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>142</width>
+		<height>170</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>5</xmin>
+			<ymin>10</ymin>
+			<xmax>137</xmax>
+			<ymax>162</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/30.jpg b/PAR 152/cone_dataset/30.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..2e9246ea00f85d535b512bfa2d24f048754a3144
Binary files /dev/null and b/PAR 152/cone_dataset/30.jpg differ
diff --git a/PAR 152/cone_dataset/30.xml b/PAR 152/cone_dataset/30.xml
new file mode 100644
index 0000000000000000000000000000000000000000..7ad1e12f8c1589093df8009ac398dcbc0c9eb609
--- /dev/null
+++ b/PAR 152/cone_dataset/30.xml	
@@ -0,0 +1,62 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>30.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\30.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>1024</width>
+		<height>768</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>1</xmin>
+			<ymin>1</ymin>
+			<xmax>297</xmax>
+			<ymax>756</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>48</xmin>
+			<ymin>1</ymin>
+			<xmax>352</xmax>
+			<ymax>523</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>183</xmin>
+			<ymin>48</ymin>
+			<xmax>384</xmax>
+			<ymax>418</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>299</xmin>
+			<ymin>125</ymin>
+			<xmax>589</xmax>
+			<ymax>360</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/31.jpg b/PAR 152/cone_dataset/31.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..f20e43d431fa9743325fec2bdd151f3eab1ddaf4
Binary files /dev/null and b/PAR 152/cone_dataset/31.jpg differ
diff --git a/PAR 152/cone_dataset/31.xml b/PAR 152/cone_dataset/31.xml
new file mode 100644
index 0000000000000000000000000000000000000000..95ea8048e389114c90ad1f35dd058f2bd85358e1
--- /dev/null
+++ b/PAR 152/cone_dataset/31.xml	
@@ -0,0 +1,38 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>31.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\31.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>478</width>
+		<height>359</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>19</xmin>
+			<ymin>32</ymin>
+			<xmax>261</xmax>
+			<ymax>309</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>268</xmin>
+			<ymin>76</ymin>
+			<xmax>438</xmax>
+			<ymax>275</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/32.jpg b/PAR 152/cone_dataset/32.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..1a88a042be91f82216ead3d534222983b5b07fe5
Binary files /dev/null and b/PAR 152/cone_dataset/32.jpg differ
diff --git a/PAR 152/cone_dataset/32.xml b/PAR 152/cone_dataset/32.xml
new file mode 100644
index 0000000000000000000000000000000000000000..0f671931ef4e20b4ce89001337764f5eedb11c20
--- /dev/null
+++ b/PAR 152/cone_dataset/32.xml	
@@ -0,0 +1,74 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>32.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\32.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>1024</width>
+		<height>768</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>105</xmin>
+			<ymin>338</ymin>
+			<xmax>289</xmax>
+			<ymax>547</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>229</xmin>
+			<ymin>393</ymin>
+			<xmax>473</xmax>
+			<ymax>594</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>538</xmin>
+			<ymin>395</ymin>
+			<xmax>737</xmax>
+			<ymax>650</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>413</xmin>
+			<ymin>347</ymin>
+			<xmax>566</xmax>
+			<ymax>498</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>650</xmin>
+			<ymin>341</ymin>
+			<xmax>800</xmax>
+			<ymax>466</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/33.jpg b/PAR 152/cone_dataset/33.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..1b9fbe5d2024e13bcecd606c29c13e89403c123d
Binary files /dev/null and b/PAR 152/cone_dataset/33.jpg differ
diff --git a/PAR 152/cone_dataset/33.xml b/PAR 152/cone_dataset/33.xml
new file mode 100644
index 0000000000000000000000000000000000000000..b18fd0d3c5481f088179262b8fbef9fd89a41aec
--- /dev/null
+++ b/PAR 152/cone_dataset/33.xml	
@@ -0,0 +1,62 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>33.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\33.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>507</width>
+		<height>338</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>83</xmin>
+			<ymin>49</ymin>
+			<xmax>258</xmax>
+			<ymax>314</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>181</xmin>
+			<ymin>56</ymin>
+			<xmax>233</xmax>
+			<ymax>191</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>206</xmin>
+			<ymin>60</ymin>
+			<xmax>250</xmax>
+			<ymax>147</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>232</xmin>
+			<ymin>62</ymin>
+			<xmax>255</xmax>
+			<ymax>116</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/34.jpg b/PAR 152/cone_dataset/34.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..19d43722c14bd38d0c451ad9cb4f23fc08a772d2
Binary files /dev/null and b/PAR 152/cone_dataset/34.jpg differ
diff --git a/PAR 152/cone_dataset/34.xml b/PAR 152/cone_dataset/34.xml
new file mode 100644
index 0000000000000000000000000000000000000000..4b39b925cc17d9c9d98fcc47f013148a7056bd25
--- /dev/null
+++ b/PAR 152/cone_dataset/34.xml	
@@ -0,0 +1,62 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>34.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\34.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>478</width>
+		<height>359</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>237</xmin>
+			<ymin>23</ymin>
+			<xmax>438</xmax>
+			<ymax>313</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>152</xmin>
+			<ymin>88</ymin>
+			<xmax>233</xmax>
+			<ymax>214</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>110</xmin>
+			<ymin>113</ymin>
+			<xmax>166</xmax>
+			<ymax>189</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>85</xmin>
+			<ymin>150</ymin>
+			<xmax>106</xmax>
+			<ymax>186</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/35.jpg b/PAR 152/cone_dataset/35.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..1956703b306b7c5937d6a8467a8c88e0cebbabf7
Binary files /dev/null and b/PAR 152/cone_dataset/35.jpg differ
diff --git a/PAR 152/cone_dataset/35.xml b/PAR 152/cone_dataset/35.xml
new file mode 100644
index 0000000000000000000000000000000000000000..4b6b98479598bb5468aef6531be82c30aa5f8de2
--- /dev/null
+++ b/PAR 152/cone_dataset/35.xml	
@@ -0,0 +1,86 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>35.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\35.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>507</width>
+		<height>338</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>305</xmin>
+			<ymin>19</ymin>
+			<xmax>480</xmax>
+			<ymax>298</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>150</xmin>
+			<ymin>175</ymin>
+			<xmax>212</xmax>
+			<ymax>262</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>125</xmin>
+			<ymin>198</ymin>
+			<xmax>157</xmax>
+			<ymax>257</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>97</xmin>
+			<ymin>215</ymin>
+			<xmax>120</xmax>
+			<ymax>254</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>86</xmin>
+			<ymin>219</ymin>
+			<xmax>101</xmax>
+			<ymax>254</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>52</xmin>
+			<ymin>229</ymin>
+			<xmax>65</xmax>
+			<ymax>249</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/36.jpg b/PAR 152/cone_dataset/36.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..a73acd285fd86c8caebfb75425e174519d45cf14
Binary files /dev/null and b/PAR 152/cone_dataset/36.jpg differ
diff --git a/PAR 152/cone_dataset/36.xml b/PAR 152/cone_dataset/36.xml
new file mode 100644
index 0000000000000000000000000000000000000000..10a88dcb45c48dfcb952ae030b23363ceabe660f
--- /dev/null
+++ b/PAR 152/cone_dataset/36.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>36.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\36.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>323</width>
+		<height>529</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>1</xmin>
+			<ymin>17</ymin>
+			<xmax>309</xmax>
+			<ymax>504</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/37.jpg b/PAR 152/cone_dataset/37.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..51d6c1901fbca1942b4f6b6c6094c3ece6d1fc31
Binary files /dev/null and b/PAR 152/cone_dataset/37.jpg differ
diff --git a/PAR 152/cone_dataset/37.xml b/PAR 152/cone_dataset/37.xml
new file mode 100644
index 0000000000000000000000000000000000000000..e9741a2467c31304a3fb39feb5e3a6b029004b29
--- /dev/null
+++ b/PAR 152/cone_dataset/37.xml	
@@ -0,0 +1,38 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>37.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\37.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>421</width>
+		<height>407</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>43</xmin>
+			<ymin>39</ymin>
+			<xmax>257</xmax>
+			<ymax>301</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>189</xmin>
+			<ymin>115</ymin>
+			<xmax>384</xmax>
+			<ymax>268</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/38.jpg b/PAR 152/cone_dataset/38.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..962eab6eb3fbf4fc309c16e02c0266f1c0e9345d
Binary files /dev/null and b/PAR 152/cone_dataset/38.jpg differ
diff --git a/PAR 152/cone_dataset/38.xml b/PAR 152/cone_dataset/38.xml
new file mode 100644
index 0000000000000000000000000000000000000000..05e3e59a73a8ed9ab78048e6b9522cc95f2fd82d
--- /dev/null
+++ b/PAR 152/cone_dataset/38.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>38.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\38.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>492</width>
+		<height>348</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>208</xmin>
+			<ymin>25</ymin>
+			<xmax>428</xmax>
+			<ymax>311</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/39.jpg b/PAR 152/cone_dataset/39.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..27aa64ebee438e321853a38b800d450ba3a3071c
Binary files /dev/null and b/PAR 152/cone_dataset/39.jpg differ
diff --git a/PAR 152/cone_dataset/39.xml b/PAR 152/cone_dataset/39.xml
new file mode 100644
index 0000000000000000000000000000000000000000..a66d6d4be73f0712cce547d699e0e885348eeae5
--- /dev/null
+++ b/PAR 152/cone_dataset/39.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>39.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\39.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>469</width>
+		<height>368</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>58</xmin>
+			<ymin>120</ymin>
+			<xmax>329</xmax>
+			<ymax>307</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/4.jpg b/PAR 152/cone_dataset/4.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..8aa37c4a7a12c29419d95a348fc3ea4bdbc33270
Binary files /dev/null and b/PAR 152/cone_dataset/4.jpg differ
diff --git a/PAR 152/cone_dataset/4.xml b/PAR 152/cone_dataset/4.xml
new file mode 100644
index 0000000000000000000000000000000000000000..6ae7150fa5243bdb61f61c2fc0d7cd3bb73770a2
--- /dev/null
+++ b/PAR 152/cone_dataset/4.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>4.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\4.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>381</width>
+		<height>454</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>14</xmin>
+			<ymin>25</ymin>
+			<xmax>365</xmax>
+			<ymax>432</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/40.jpg b/PAR 152/cone_dataset/40.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..963bd3acaec85a2c7e7ee23cca4459fba5e84dcf
Binary files /dev/null and b/PAR 152/cone_dataset/40.jpg differ
diff --git a/PAR 152/cone_dataset/40.xml b/PAR 152/cone_dataset/40.xml
new file mode 100644
index 0000000000000000000000000000000000000000..54d5cd8d132225e1425f56d52a92b60c73357d51
--- /dev/null
+++ b/PAR 152/cone_dataset/40.xml	
@@ -0,0 +1,146 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>40.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\40.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>507</width>
+		<height>338</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>51</xmin>
+			<ymin>125</ymin>
+			<xmax>149</xmax>
+			<ymax>300</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>147</xmin>
+			<ymin>108</ymin>
+			<xmax>230</xmax>
+			<ymax>247</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>54</xmin>
+			<ymin>12</ymin>
+			<xmax>84</xmax>
+			<ymax>57</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>150</xmin>
+			<ymin>10</ymin>
+			<xmax>172</xmax>
+			<ymax>48</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>218</xmin>
+			<ymin>8</ymin>
+			<xmax>241</xmax>
+			<ymax>43</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>444</xmin>
+			<ymin>43</ymin>
+			<xmax>479</xmax>
+			<ymax>101</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>305</xmin>
+			<ymin>156</ymin>
+			<xmax>407</xmax>
+			<ymax>254</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>275</xmin>
+			<ymin>10</ymin>
+			<xmax>293</xmax>
+			<ymax>38</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>485</xmin>
+			<ymin>35</ymin>
+			<xmax>507</xmax>
+			<ymax>82</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>314</xmin>
+			<ymin>9</ymin>
+			<xmax>328</xmax>
+			<ymax>35</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>354</xmin>
+			<ymin>9</ymin>
+			<xmax>368</xmax>
+			<ymax>33</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/41.jpg b/PAR 152/cone_dataset/41.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..3c384b8ef14a07741777d8467d0f3f01ea296d92
Binary files /dev/null and b/PAR 152/cone_dataset/41.jpg differ
diff --git a/PAR 152/cone_dataset/41.xml b/PAR 152/cone_dataset/41.xml
new file mode 100644
index 0000000000000000000000000000000000000000..c49262c9497ef61c6eb8ce92a5ed051d82039973
--- /dev/null
+++ b/PAR 152/cone_dataset/41.xml	
@@ -0,0 +1,122 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>41.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\41.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>529</width>
+		<height>327</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>45</xmin>
+			<ymin>155</ymin>
+			<xmax>149</xmax>
+			<ymax>319</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>152</xmin>
+			<ymin>128</ymin>
+			<xmax>241</xmax>
+			<ymax>277</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>228</xmin>
+			<ymin>106</ymin>
+			<xmax>311</xmax>
+			<ymax>247</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>295</xmin>
+			<ymin>90</ymin>
+			<xmax>368</xmax>
+			<ymax>227</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>339</xmin>
+			<ymin>72</ymin>
+			<xmax>407</xmax>
+			<ymax>200</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>381</xmin>
+			<ymin>61</ymin>
+			<xmax>444</xmax>
+			<ymax>184</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>420</xmin>
+			<ymin>47</ymin>
+			<xmax>477</xmax>
+			<ymax>165</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>464</xmin>
+			<ymin>35</ymin>
+			<xmax>520</xmax>
+			<ymax>145</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>496</xmin>
+			<ymin>22</ymin>
+			<xmax>529</xmax>
+			<ymax>123</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/42.jpg b/PAR 152/cone_dataset/42.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..f4ac7318edb7e8b07d4cc4055d21b9d8040ce91b
Binary files /dev/null and b/PAR 152/cone_dataset/42.jpg differ
diff --git a/PAR 152/cone_dataset/42.xml b/PAR 152/cone_dataset/42.xml
new file mode 100644
index 0000000000000000000000000000000000000000..695e55e16778cb35ea2b99b4b5be45e154091f3e
--- /dev/null
+++ b/PAR 152/cone_dataset/42.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>42.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\42.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>338</width>
+		<height>507</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>66</xmin>
+			<ymin>11</ymin>
+			<xmax>293</xmax>
+			<ymax>492</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/43.jpg b/PAR 152/cone_dataset/43.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..d06cf0f3ea474fc47341b89f6b18790ce3b2d689
Binary files /dev/null and b/PAR 152/cone_dataset/43.jpg differ
diff --git a/PAR 152/cone_dataset/43.xml b/PAR 152/cone_dataset/43.xml
new file mode 100644
index 0000000000000000000000000000000000000000..0feb64e1c1383bfefcce73cc193b471ca31805ff
--- /dev/null
+++ b/PAR 152/cone_dataset/43.xml	
@@ -0,0 +1,62 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>43.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\43.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>507</width>
+		<height>338</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>126</xmin>
+			<ymin>267</ymin>
+			<xmax>139</xmax>
+			<ymax>285</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>391</xmin>
+			<ymin>261</ymin>
+			<xmax>402</xmax>
+			<ymax>282</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>264</xmin>
+			<ymin>260</ymin>
+			<xmax>277</xmax>
+			<ymax>281</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>194</xmin>
+			<ymin>261</ymin>
+			<xmax>208</xmax>
+			<ymax>282</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/44.jpg b/PAR 152/cone_dataset/44.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..7b307cc905f9a83e7a709326c0a8806977c764f1
Binary files /dev/null and b/PAR 152/cone_dataset/44.jpg differ
diff --git a/PAR 152/cone_dataset/44.xml b/PAR 152/cone_dataset/44.xml
new file mode 100644
index 0000000000000000000000000000000000000000..24bc004131fd66c71d6b739a48f1e22b89a604d3
--- /dev/null
+++ b/PAR 152/cone_dataset/44.xml	
@@ -0,0 +1,50 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>44.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\44.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>337</width>
+		<height>508</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>132</xmin>
+			<ymin>37</ymin>
+			<xmax>275</xmax>
+			<ymax>302</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>2</xmin>
+			<ymin>283</ymin>
+			<xmax>99</xmax>
+			<ymax>508</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>107</xmin>
+			<ymin>1</ymin>
+			<xmax>187</xmax>
+			<ymax>95</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/45.jpg b/PAR 152/cone_dataset/45.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..4c4f5024c87a38fe492c1c3d982da849011adea3
Binary files /dev/null and b/PAR 152/cone_dataset/45.jpg differ
diff --git a/PAR 152/cone_dataset/45.xml b/PAR 152/cone_dataset/45.xml
new file mode 100644
index 0000000000000000000000000000000000000000..9fcb50f9ca3c734fa55c734247bb72039fb79423
--- /dev/null
+++ b/PAR 152/cone_dataset/45.xml	
@@ -0,0 +1,98 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>45.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\45.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>594</width>
+		<height>396</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>256</xmin>
+			<ymin>264</ymin>
+			<xmax>308</xmax>
+			<ymax>347</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>115</xmin>
+			<ymin>249</ymin>
+			<xmax>154</xmax>
+			<ymax>317</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>15</xmin>
+			<ymin>240</ymin>
+			<xmax>53</xmax>
+			<ymax>293</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>507</xmin>
+			<ymin>283</ymin>
+			<xmax>573</xmax>
+			<ymax>391</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>347</xmin>
+			<ymin>192</ymin>
+			<xmax>359</xmax>
+			<ymax>209</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>289</xmin>
+			<ymin>190</ymin>
+			<xmax>302</xmax>
+			<ymax>209</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>102</xmin>
+			<ymin>194</ymin>
+			<xmax>111</xmax>
+			<ymax>206</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/46.jpg b/PAR 152/cone_dataset/46.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..7ca6552e5e7c57a6c699da7236addae19293c80a
Binary files /dev/null and b/PAR 152/cone_dataset/46.jpg differ
diff --git a/PAR 152/cone_dataset/46.xml b/PAR 152/cone_dataset/46.xml
new file mode 100644
index 0000000000000000000000000000000000000000..04029dac98ce8d36867c69aa932cc8ef5a6da83c
--- /dev/null
+++ b/PAR 152/cone_dataset/46.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>46.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\46.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>338</width>
+		<height>507</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>132</xmin>
+			<ymin>162</ymin>
+			<xmax>304</xmax>
+			<ymax>478</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/47.jpg b/PAR 152/cone_dataset/47.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..2cf705c85041f072f9000e3fef20f3f7989e6bf6
Binary files /dev/null and b/PAR 152/cone_dataset/47.jpg differ
diff --git a/PAR 152/cone_dataset/47.xml b/PAR 152/cone_dataset/47.xml
new file mode 100644
index 0000000000000000000000000000000000000000..3e3aa3d1a0ccc16f7695e16d4739e5028c9a5c5e
--- /dev/null
+++ b/PAR 152/cone_dataset/47.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>47.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\47.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>478</width>
+		<height>359</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>103</xmin>
+			<ymin>11</ymin>
+			<xmax>384</xmax>
+			<ymax>322</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/48.jpg b/PAR 152/cone_dataset/48.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..eb62e66ad07dd40e16e197f2888339f684f1c72d
Binary files /dev/null and b/PAR 152/cone_dataset/48.jpg differ
diff --git a/PAR 152/cone_dataset/48.xml b/PAR 152/cone_dataset/48.xml
new file mode 100644
index 0000000000000000000000000000000000000000..68b1fdd321847c9d06d4e7e8c343be44866c4465
--- /dev/null
+++ b/PAR 152/cone_dataset/48.xml	
@@ -0,0 +1,62 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>48.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\48.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>514</width>
+		<height>333</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>16</xmin>
+			<ymin>23</ymin>
+			<xmax>80</xmax>
+			<ymax>135</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>113</xmin>
+			<ymin>34</ymin>
+			<xmax>190</xmax>
+			<ymax>164</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>375</xmin>
+			<ymin>90</ymin>
+			<xmax>506</xmax>
+			<ymax>291</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>205</xmin>
+			<ymin>165</ymin>
+			<xmax>363</xmax>
+			<ymax>301</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/49.jpg b/PAR 152/cone_dataset/49.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..7673a3353051954e73afef7ab2a4692fd625a7c8
Binary files /dev/null and b/PAR 152/cone_dataset/49.jpg differ
diff --git a/PAR 152/cone_dataset/49.xml b/PAR 152/cone_dataset/49.xml
new file mode 100644
index 0000000000000000000000000000000000000000..7a3761ffd47ef6872354eae63391148063096287
--- /dev/null
+++ b/PAR 152/cone_dataset/49.xml	
@@ -0,0 +1,38 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>49.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\49.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>594</width>
+		<height>334</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>27</xmin>
+			<ymin>44</ymin>
+			<xmax>212</xmax>
+			<ymax>283</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>369</xmin>
+			<ymin>9</ymin>
+			<xmax>589</xmax>
+			<ymax>292</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/5.jpg b/PAR 152/cone_dataset/5.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..391a91991b7b15f907cbe9e240943914afc28de9
Binary files /dev/null and b/PAR 152/cone_dataset/5.jpg differ
diff --git a/PAR 152/cone_dataset/5.xml b/PAR 152/cone_dataset/5.xml
new file mode 100644
index 0000000000000000000000000000000000000000..5b963a42f1c4e195a6b3a367d1566c326ea3f5e6
--- /dev/null
+++ b/PAR 152/cone_dataset/5.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>5.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\5.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>337</width>
+		<height>508</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>5</xmin>
+			<ymin>38</ymin>
+			<xmax>336</xmax>
+			<ymax>478</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/50.jpg b/PAR 152/cone_dataset/50.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..5de0b10d1627f61e7528d6b5325e3e4733e7b497
Binary files /dev/null and b/PAR 152/cone_dataset/50.jpg differ
diff --git a/PAR 152/cone_dataset/50.xml b/PAR 152/cone_dataset/50.xml
new file mode 100644
index 0000000000000000000000000000000000000000..c396b6031b259f2cf9a0af8bdef12cd59eb45594
--- /dev/null
+++ b/PAR 152/cone_dataset/50.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>50.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\50.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>337</width>
+		<height>508</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>53</xmin>
+			<ymin>47</ymin>
+			<xmax>259</xmax>
+			<ymax>456</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/51.jpg b/PAR 152/cone_dataset/51.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..5727012fa105123957c9e42e4b6ab53a5b9cbf7a
Binary files /dev/null and b/PAR 152/cone_dataset/51.jpg differ
diff --git a/PAR 152/cone_dataset/51.xml b/PAR 152/cone_dataset/51.xml
new file mode 100644
index 0000000000000000000000000000000000000000..16d57f31d918a02bda72df976e45fcce5e519c3a
--- /dev/null
+++ b/PAR 152/cone_dataset/51.xml	
@@ -0,0 +1,74 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>51.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\51.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>594</width>
+		<height>401</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>378</xmin>
+			<ymin>246</ymin>
+			<xmax>411</xmax>
+			<ymax>286</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>22</xmin>
+			<ymin>292</ymin>
+			<xmax>79</xmax>
+			<ymax>368</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>178</xmin>
+			<ymin>257</ymin>
+			<xmax>205</xmax>
+			<ymax>309</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>113</xmin>
+			<ymin>263</ymin>
+			<xmax>145</xmax>
+			<ymax>321</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>79</xmin>
+			<ymin>275</ymin>
+			<xmax>117</xmax>
+			<ymax>339</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/52.jpg b/PAR 152/cone_dataset/52.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..5c79c02e9b15f8fabc13d74dc185f0d202bf32bf
Binary files /dev/null and b/PAR 152/cone_dataset/52.jpg differ
diff --git a/PAR 152/cone_dataset/52.xml b/PAR 152/cone_dataset/52.xml
new file mode 100644
index 0000000000000000000000000000000000000000..80a8ffbf2fac7cd8c62660bea200651d6512c30c
--- /dev/null
+++ b/PAR 152/cone_dataset/52.xml	
@@ -0,0 +1,38 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>52.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\52.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>170</width>
+		<height>106</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>1</xmin>
+			<ymin>1</ymin>
+			<xmax>97</xmax>
+			<ymax>103</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>35</xmin>
+			<ymin>16</ymin>
+			<xmax>161</xmax>
+			<ymax>104</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/53.jpg b/PAR 152/cone_dataset/53.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..09d260081ed1d3f2288b6e689fb7c0752f4b279f
Binary files /dev/null and b/PAR 152/cone_dataset/53.jpg differ
diff --git a/PAR 152/cone_dataset/53.xml b/PAR 152/cone_dataset/53.xml
new file mode 100644
index 0000000000000000000000000000000000000000..a86fb83d76693f31bba0053ec6e6486359612f54
--- /dev/null
+++ b/PAR 152/cone_dataset/53.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>53.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\53.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>114</width>
+		<height>170</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>17</xmin>
+			<ymin>19</ymin>
+			<xmax>92</xmax>
+			<ymax>158</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/54.jpg b/PAR 152/cone_dataset/54.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..6ee6c1d9ca20d6de8cc7a63e66400baebdce72d1
Binary files /dev/null and b/PAR 152/cone_dataset/54.jpg differ
diff --git a/PAR 152/cone_dataset/54.xml b/PAR 152/cone_dataset/54.xml
new file mode 100644
index 0000000000000000000000000000000000000000..7e059368607fb0a24950911c4330ea9ef7128013
--- /dev/null
+++ b/PAR 152/cone_dataset/54.xml	
@@ -0,0 +1,62 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>54.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\54.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>416</width>
+		<height>416</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>309</xmin>
+			<ymin>158</ymin>
+			<xmax>383</xmax>
+			<ymax>264</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>13</xmin>
+			<ymin>161</ymin>
+			<xmax>85</xmax>
+			<ymax>264</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>34</xmin>
+			<ymin>90</ymin>
+			<xmax>95</xmax>
+			<ymax>166</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>276</xmin>
+			<ymin>82</ymin>
+			<xmax>324</xmax>
+			<ymax>156</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/55.jpg b/PAR 152/cone_dataset/55.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..90ef67dca44063a2eaada849f5ecb271bf67f229
Binary files /dev/null and b/PAR 152/cone_dataset/55.jpg differ
diff --git a/PAR 152/cone_dataset/55.xml b/PAR 152/cone_dataset/55.xml
new file mode 100644
index 0000000000000000000000000000000000000000..870124701ab6dad222cde82a5b397f7ba9df93b3
--- /dev/null
+++ b/PAR 152/cone_dataset/55.xml	
@@ -0,0 +1,38 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>55.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\55.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>478</width>
+		<height>359</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>177</xmin>
+			<ymin>44</ymin>
+			<xmax>355</xmax>
+			<ymax>286</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>377</xmin>
+			<ymin>164</ymin>
+			<xmax>448</xmax>
+			<ymax>261</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/56.jpg b/PAR 152/cone_dataset/56.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..3de4d3adc6560de9f1e4a9253330b48ee09d92b1
Binary files /dev/null and b/PAR 152/cone_dataset/56.jpg differ
diff --git a/PAR 152/cone_dataset/56.xml b/PAR 152/cone_dataset/56.xml
new file mode 100644
index 0000000000000000000000000000000000000000..256b78685e7e218cb345c084807faa46516a4a09
--- /dev/null
+++ b/PAR 152/cone_dataset/56.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>56.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\56.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>422</width>
+		<height>407</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>79</xmin>
+			<ymin>10</ymin>
+			<xmax>306</xmax>
+			<ymax>314</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/57.jpg b/PAR 152/cone_dataset/57.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..1159b708018d81ac10869de4849a54afe43e0c63
Binary files /dev/null and b/PAR 152/cone_dataset/57.jpg differ
diff --git a/PAR 152/cone_dataset/57.xml b/PAR 152/cone_dataset/57.xml
new file mode 100644
index 0000000000000000000000000000000000000000..70c7754a80ddad37f42d2c093976b8d357f52bb3
--- /dev/null
+++ b/PAR 152/cone_dataset/57.xml	
@@ -0,0 +1,86 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>57.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\57.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>414</width>
+		<height>414</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>23</xmin>
+			<ymin>229</ymin>
+			<xmax>158</xmax>
+			<ymax>394</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>171</xmin>
+			<ymin>190</ymin>
+			<xmax>267</xmax>
+			<ymax>307</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>257</xmin>
+			<ymin>168</ymin>
+			<xmax>330</xmax>
+			<ymax>260</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>311</xmin>
+			<ymin>155</ymin>
+			<xmax>372</xmax>
+			<ymax>229</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>355</xmin>
+			<ymin>145</ymin>
+			<xmax>401</xmax>
+			<ymax>208</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>388</xmin>
+			<ymin>138</ymin>
+			<xmax>414</xmax>
+			<ymax>191</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/58.jpg b/PAR 152/cone_dataset/58.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..9afdc7e3357fb32fb9c75b44399dbea68bbb9dd1
Binary files /dev/null and b/PAR 152/cone_dataset/58.jpg differ
diff --git a/PAR 152/cone_dataset/58.xml b/PAR 152/cone_dataset/58.xml
new file mode 100644
index 0000000000000000000000000000000000000000..d7e5b8a5b216526919367bd777880bcca9c78b82
--- /dev/null
+++ b/PAR 152/cone_dataset/58.xml	
@@ -0,0 +1,38 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>58.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\58.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>369</width>
+		<height>464</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>34</xmin>
+			<ymin>171</ymin>
+			<xmax>193</xmax>
+			<ymax>433</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>148</xmin>
+			<ymin>174</ymin>
+			<xmax>339</xmax>
+			<ymax>284</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/59.jpg b/PAR 152/cone_dataset/59.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..960c455c0a11635108fccdb1a77a675dbc623be5
Binary files /dev/null and b/PAR 152/cone_dataset/59.jpg differ
diff --git a/PAR 152/cone_dataset/59.xml b/PAR 152/cone_dataset/59.xml
new file mode 100644
index 0000000000000000000000000000000000000000..57a3eb5e6d8ed886b287f24400929c36df87820a
--- /dev/null
+++ b/PAR 152/cone_dataset/59.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>59.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\59.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>359</width>
+		<height>478</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>53</xmin>
+			<ymin>57</ymin>
+			<xmax>292</xmax>
+			<ymax>434</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/6.jpg b/PAR 152/cone_dataset/6.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..a39ba1e368e2a384d4a8a83fcc3eb50bae5005a5
Binary files /dev/null and b/PAR 152/cone_dataset/6.jpg differ
diff --git a/PAR 152/cone_dataset/6.xml b/PAR 152/cone_dataset/6.xml
new file mode 100644
index 0000000000000000000000000000000000000000..30164432ff2f73dbdb5dd172572dc99ea59458e7
--- /dev/null
+++ b/PAR 152/cone_dataset/6.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>6.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\6.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>337</width>
+		<height>508</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>6</xmin>
+			<ymin>39</ymin>
+			<xmax>332</xmax>
+			<ymax>468</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/60.jpg b/PAR 152/cone_dataset/60.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..87327bce4c2eedb637ace032229ab8a3517e81aa
Binary files /dev/null and b/PAR 152/cone_dataset/60.jpg differ
diff --git a/PAR 152/cone_dataset/60.xml b/PAR 152/cone_dataset/60.xml
new file mode 100644
index 0000000000000000000000000000000000000000..fd820d8c78151e52d0ffb4fc0bbc1cac9db90e2e
--- /dev/null
+++ b/PAR 152/cone_dataset/60.xml	
@@ -0,0 +1,86 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>60.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\60.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>482</width>
+		<height>355</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>50</xmin>
+			<ymin>148</ymin>
+			<xmax>195</xmax>
+			<ymax>337</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>212</xmin>
+			<ymin>109</ymin>
+			<xmax>317</xmax>
+			<ymax>235</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>306</xmin>
+			<ymin>81</ymin>
+			<xmax>389</xmax>
+			<ymax>182</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>368</xmin>
+			<ymin>67</ymin>
+			<xmax>434</xmax>
+			<ymax>149</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>422</xmin>
+			<ymin>54</ymin>
+			<xmax>468</xmax>
+			<ymax>125</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>454</xmin>
+			<ymin>48</ymin>
+			<xmax>482</xmax>
+			<ymax>105</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/61.jpg b/PAR 152/cone_dataset/61.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..86b02e0acffe271cb984dafd06f6137b8a9726bc
Binary files /dev/null and b/PAR 152/cone_dataset/61.jpg differ
diff --git a/PAR 152/cone_dataset/61.xml b/PAR 152/cone_dataset/61.xml
new file mode 100644
index 0000000000000000000000000000000000000000..234d206c51ad03671c775d091469d1f1a03c1427
--- /dev/null
+++ b/PAR 152/cone_dataset/61.xml	
@@ -0,0 +1,50 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>61.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\61.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>507</width>
+		<height>338</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>57</xmin>
+			<ymin>168</ymin>
+			<xmax>166</xmax>
+			<ymax>319</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>69</xmin>
+			<ymin>125</ymin>
+			<xmax>143</xmax>
+			<ymax>225</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>96</xmin>
+			<ymin>73</ymin>
+			<xmax>139</xmax>
+			<ymax>134</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/62.jpg b/PAR 152/cone_dataset/62.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..db7b81dc3917af4558b09d11029e5ffbd0497828
Binary files /dev/null and b/PAR 152/cone_dataset/62.jpg differ
diff --git a/PAR 152/cone_dataset/62.xml b/PAR 152/cone_dataset/62.xml
new file mode 100644
index 0000000000000000000000000000000000000000..6c5e86da09fdff2eda112e4ac2dbefe0b21e3aac
--- /dev/null
+++ b/PAR 152/cone_dataset/62.xml	
@@ -0,0 +1,50 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>62.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\62.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>478</width>
+		<height>359</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>249</xmin>
+			<ymin>182</ymin>
+			<xmax>346</xmax>
+			<ymax>303</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>107</xmin>
+			<ymin>189</ymin>
+			<xmax>223</xmax>
+			<ymax>272</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>48</xmin>
+			<ymin>155</ymin>
+			<xmax>128</xmax>
+			<ymax>259</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/63.jpg b/PAR 152/cone_dataset/63.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..60a029dcd62bd5126266adae7ab4d9405daa6ed2
Binary files /dev/null and b/PAR 152/cone_dataset/63.jpg differ
diff --git a/PAR 152/cone_dataset/63.xml b/PAR 152/cone_dataset/63.xml
new file mode 100644
index 0000000000000000000000000000000000000000..8a22ec396b8b851a79249caf1320e387e82e050f
--- /dev/null
+++ b/PAR 152/cone_dataset/63.xml	
@@ -0,0 +1,62 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>63.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\63.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>478</width>
+		<height>359</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>292</xmin>
+			<ymin>202</ymin>
+			<xmax>398</xmax>
+			<ymax>293</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>130</xmin>
+			<ymin>168</ymin>
+			<xmax>222</xmax>
+			<ymax>270</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>52</xmin>
+			<ymin>210</ymin>
+			<xmax>153</xmax>
+			<ymax>335</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>183</xmin>
+			<ymin>153</ymin>
+			<xmax>256</xmax>
+			<ymax>218</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/64.jpg b/PAR 152/cone_dataset/64.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..86f5399311c51cc329fb7d8fd103c7a80992930f
Binary files /dev/null and b/PAR 152/cone_dataset/64.jpg differ
diff --git a/PAR 152/cone_dataset/64.xml b/PAR 152/cone_dataset/64.xml
new file mode 100644
index 0000000000000000000000000000000000000000..856ed467912123196397451dcd2a4559c0a84b61
--- /dev/null
+++ b/PAR 152/cone_dataset/64.xml	
@@ -0,0 +1,62 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>64.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\64.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>359</width>
+		<height>479</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>171</xmin>
+			<ymin>26</ymin>
+			<xmax>254</xmax>
+			<ymax>178</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>2</xmin>
+			<ymin>179</ymin>
+			<xmax>189</xmax>
+			<ymax>474</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>252</xmin>
+			<ymin>1</ymin>
+			<xmax>301</xmax>
+			<ymax>85</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>286</xmin>
+			<ymin>1</ymin>
+			<xmax>322</xmax>
+			<ymax>43</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/65.jpg b/PAR 152/cone_dataset/65.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..3ff2198fe242735d5e87fa5b778563d65db8f116
Binary files /dev/null and b/PAR 152/cone_dataset/65.jpg differ
diff --git a/PAR 152/cone_dataset/65.xml b/PAR 152/cone_dataset/65.xml
new file mode 100644
index 0000000000000000000000000000000000000000..802484edee2522128e25d51cbe357c1673f5f584
--- /dev/null
+++ b/PAR 152/cone_dataset/65.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>65.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\65.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>339</width>
+		<height>509</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>24</xmin>
+			<ymin>58</ymin>
+			<xmax>318</xmax>
+			<ymax>471</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/66.jpg b/PAR 152/cone_dataset/66.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..d0cb30d523cc494bb94692e434850f3b15013005
Binary files /dev/null and b/PAR 152/cone_dataset/66.jpg differ
diff --git a/PAR 152/cone_dataset/66.xml b/PAR 152/cone_dataset/66.xml
new file mode 100644
index 0000000000000000000000000000000000000000..16f99d0bc13f4cb7a6fae51962fdccf12564eee3
--- /dev/null
+++ b/PAR 152/cone_dataset/66.xml	
@@ -0,0 +1,38 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>66.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\66.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>1024</width>
+		<height>680</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>69</xmin>
+			<ymin>257</ymin>
+			<xmax>329</xmax>
+			<ymax>648</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>649</xmin>
+			<ymin>239</ymin>
+			<xmax>922</xmax>
+			<ymax>621</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/67.jpg b/PAR 152/cone_dataset/67.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..c94a5d54e91d8d3c16656a67e8ca24f76118ab45
Binary files /dev/null and b/PAR 152/cone_dataset/67.jpg differ
diff --git a/PAR 152/cone_dataset/67.xml b/PAR 152/cone_dataset/67.xml
new file mode 100644
index 0000000000000000000000000000000000000000..f0843121c0f2f17800fad45f4e84d9280d95d58a
--- /dev/null
+++ b/PAR 152/cone_dataset/67.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>67.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\67.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>356</width>
+		<height>481</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>106</xmin>
+			<ymin>88</ymin>
+			<xmax>241</xmax>
+			<ymax>316</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/68.jpg b/PAR 152/cone_dataset/68.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..c0cc64b7acfc6533991db4630656471405630760
Binary files /dev/null and b/PAR 152/cone_dataset/68.jpg differ
diff --git a/PAR 152/cone_dataset/68.xml b/PAR 152/cone_dataset/68.xml
new file mode 100644
index 0000000000000000000000000000000000000000..7cb60fc813748d489aa9e7022bc9f1beb14b4cd2
--- /dev/null
+++ b/PAR 152/cone_dataset/68.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>68.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\68.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>478</width>
+		<height>359</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>221</xmin>
+			<ymin>110</ymin>
+			<xmax>259</xmax>
+			<ymax>179</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/69.jpg b/PAR 152/cone_dataset/69.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..3fc33a9fc2e82752469d34c91782f850f0eff22a
Binary files /dev/null and b/PAR 152/cone_dataset/69.jpg differ
diff --git a/PAR 152/cone_dataset/69.xml b/PAR 152/cone_dataset/69.xml
new file mode 100644
index 0000000000000000000000000000000000000000..b13e86158c90b864f77db1ff8af3b28c750e8dfc
--- /dev/null
+++ b/PAR 152/cone_dataset/69.xml	
@@ -0,0 +1,38 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>69.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\69.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>478</width>
+		<height>359</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>28</xmin>
+			<ymin>30</ymin>
+			<xmax>240</xmax>
+			<ymax>302</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>178</xmin>
+			<ymin>99</ymin>
+			<xmax>452</xmax>
+			<ymax>312</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/7.jpg b/PAR 152/cone_dataset/7.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..55c95a5f157d99f295fd8c7110455a7764e3a1f3
Binary files /dev/null and b/PAR 152/cone_dataset/7.jpg differ
diff --git a/PAR 152/cone_dataset/7.xml b/PAR 152/cone_dataset/7.xml
new file mode 100644
index 0000000000000000000000000000000000000000..24cdb64b5e9aeeeab9b3272251a371bd210d5955
--- /dev/null
+++ b/PAR 152/cone_dataset/7.xml	
@@ -0,0 +1,98 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>7.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\7.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>507</width>
+		<height>338</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>280</xmin>
+			<ymin>45</ymin>
+			<xmax>438</xmax>
+			<ymax>274</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>79</xmin>
+			<ymin>150</ymin>
+			<xmax>100</xmax>
+			<ymax>186</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>167</xmin>
+			<ymin>154</ymin>
+			<xmax>187</xmax>
+			<ymax>184</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>203</xmin>
+			<ymin>152</ymin>
+			<xmax>219</xmax>
+			<ymax>187</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>255</xmin>
+			<ymin>114</ymin>
+			<xmax>310</xmax>
+			<ymax>221</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>245</xmin>
+			<ymin>137</ymin>
+			<xmax>269</xmax>
+			<ymax>201</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>231</xmin>
+			<ymin>145</ymin>
+			<xmax>250</xmax>
+			<ymax>195</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/70.jpg b/PAR 152/cone_dataset/70.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..dab53d9cf82b01a0387f327cf5657aee5c6cfd57
Binary files /dev/null and b/PAR 152/cone_dataset/70.jpg differ
diff --git a/PAR 152/cone_dataset/70.xml b/PAR 152/cone_dataset/70.xml
new file mode 100644
index 0000000000000000000000000000000000000000..0f9684ad168cc5b2038e22b2b070e9da5e38feee
--- /dev/null
+++ b/PAR 152/cone_dataset/70.xml	
@@ -0,0 +1,62 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>70.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\70.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>363</width>
+		<height>473</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>73</xmin>
+			<ymin>232</ymin>
+			<xmax>155</xmax>
+			<ymax>405</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>247</xmin>
+			<ymin>72</ymin>
+			<xmax>323</xmax>
+			<ymax>223</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>188</xmin>
+			<ymin>111</ymin>
+			<xmax>251</xmax>
+			<ymax>269</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>126</xmin>
+			<ymin>166</ymin>
+			<xmax>203</xmax>
+			<ymax>330</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/71.jpg b/PAR 152/cone_dataset/71.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..17c71595c09e52093feda39ded6af86e24358b41
Binary files /dev/null and b/PAR 152/cone_dataset/71.jpg differ
diff --git a/PAR 152/cone_dataset/71.xml b/PAR 152/cone_dataset/71.xml
new file mode 100644
index 0000000000000000000000000000000000000000..64c7bf357912453265f66eb997af4b62b6a32e6f
--- /dev/null
+++ b/PAR 152/cone_dataset/71.xml	
@@ -0,0 +1,50 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>71.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\71.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>412</width>
+		<height>415</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>177</xmin>
+			<ymin>69</ymin>
+			<xmax>373</xmax>
+			<ymax>393</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>65</xmin>
+			<ymin>166</ymin>
+			<xmax>125</xmax>
+			<ymax>255</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>342</xmin>
+			<ymin>152</ymin>
+			<xmax>412</xmax>
+			<ymax>265</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/72.jpg b/PAR 152/cone_dataset/72.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..7ca6552e5e7c57a6c699da7236addae19293c80a
Binary files /dev/null and b/PAR 152/cone_dataset/72.jpg differ
diff --git a/PAR 152/cone_dataset/72.xml b/PAR 152/cone_dataset/72.xml
new file mode 100644
index 0000000000000000000000000000000000000000..0d61d6cbdfd581ccfb81b5b97ceffd1645af1d96
--- /dev/null
+++ b/PAR 152/cone_dataset/72.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>72.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\72.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>338</width>
+		<height>507</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>129</xmin>
+			<ymin>159</ymin>
+			<xmax>301</xmax>
+			<ymax>476</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/73.jpg b/PAR 152/cone_dataset/73.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..f96929d640efb84c3cce5e2bc250072aea5158be
Binary files /dev/null and b/PAR 152/cone_dataset/73.jpg differ
diff --git a/PAR 152/cone_dataset/73.xml b/PAR 152/cone_dataset/73.xml
new file mode 100644
index 0000000000000000000000000000000000000000..0f8f8107f3f7eb860d2bee386606af66ba915800
--- /dev/null
+++ b/PAR 152/cone_dataset/73.xml	
@@ -0,0 +1,98 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>73.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\73.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>604</width>
+		<height>283</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>268</xmin>
+			<ymin>113</ymin>
+			<xmax>315</xmax>
+			<ymax>171</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>185</xmin>
+			<ymin>113</ymin>
+			<xmax>233</xmax>
+			<ymax>170</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>103</xmin>
+			<ymin>114</ymin>
+			<xmax>150</xmax>
+			<ymax>170</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>15</xmin>
+			<ymin>114</ymin>
+			<xmax>65</xmax>
+			<ymax>170</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>351</xmin>
+			<ymin>114</ymin>
+			<xmax>402</xmax>
+			<ymax>171</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>440</xmin>
+			<ymin>114</ymin>
+			<xmax>487</xmax>
+			<ymax>171</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>528</xmin>
+			<ymin>116</ymin>
+			<xmax>575</xmax>
+			<ymax>171</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/74.jpg b/PAR 152/cone_dataset/74.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..d42eb9dcfaf5bac9e6e639137ee778a466f3987c
Binary files /dev/null and b/PAR 152/cone_dataset/74.jpg differ
diff --git a/PAR 152/cone_dataset/74.xml b/PAR 152/cone_dataset/74.xml
new file mode 100644
index 0000000000000000000000000000000000000000..6488c5d6052cf3612c807ce9c8a2691c889bedbe
--- /dev/null
+++ b/PAR 152/cone_dataset/74.xml	
@@ -0,0 +1,74 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>74.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\74.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>509</width>
+		<height>339</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>50</xmin>
+			<ymin>1</ymin>
+			<xmax>183</xmax>
+			<ymax>188</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>253</xmin>
+			<ymin>64</ymin>
+			<xmax>366</xmax>
+			<ymax>268</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>339</xmin>
+			<ymin>7</ymin>
+			<xmax>429</xmax>
+			<ymax>179</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>127</xmin>
+			<ymin>99</ymin>
+			<xmax>259</xmax>
+			<ymax>335</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>425</xmin>
+			<ymin>4</ymin>
+			<xmax>509</xmax>
+			<ymax>171</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/75.jpg b/PAR 152/cone_dataset/75.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..57c98cd0ec0b3552619e7ba263564025a7179dda
Binary files /dev/null and b/PAR 152/cone_dataset/75.jpg differ
diff --git a/PAR 152/cone_dataset/75.xml b/PAR 152/cone_dataset/75.xml
new file mode 100644
index 0000000000000000000000000000000000000000..efa46fc84bf45338d5f506073e56a6f3b19b6ed7
--- /dev/null
+++ b/PAR 152/cone_dataset/75.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>75.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\75.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>416</width>
+		<height>416</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>52</xmin>
+			<ymin>16</ymin>
+			<xmax>354</xmax>
+			<ymax>393</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/76.jpg b/PAR 152/cone_dataset/76.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..e7cce22d3a2901dff01f473601f3538a4047d5bd
Binary files /dev/null and b/PAR 152/cone_dataset/76.jpg differ
diff --git a/PAR 152/cone_dataset/76.xml b/PAR 152/cone_dataset/76.xml
new file mode 100644
index 0000000000000000000000000000000000000000..63c748a0e852e2fa76d55778d2cd946a5781f3b3
--- /dev/null
+++ b/PAR 152/cone_dataset/76.xml	
@@ -0,0 +1,38 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>76.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\76.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>492</width>
+		<height>348</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>80</xmin>
+			<ymin>49</ymin>
+			<xmax>295</xmax>
+			<ymax>298</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>216</xmin>
+			<ymin>81</ymin>
+			<xmax>399</xmax>
+			<ymax>271</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/77.jpg b/PAR 152/cone_dataset/77.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..2cadc057f4c36a8585f5d7b90dee4d5cf2945ab1
Binary files /dev/null and b/PAR 152/cone_dataset/77.jpg differ
diff --git a/PAR 152/cone_dataset/77.xml b/PAR 152/cone_dataset/77.xml
new file mode 100644
index 0000000000000000000000000000000000000000..0a2ab193a08b57922c41fff5876dc24c38cfed0b
--- /dev/null
+++ b/PAR 152/cone_dataset/77.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>77.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\77.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>387</width>
+		<height>443</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>8</xmin>
+			<ymin>1</ymin>
+			<xmax>378</xmax>
+			<ymax>441</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/78.jpg b/PAR 152/cone_dataset/78.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..f5610066fdc2e0127805545d4ead5edf2bc41c93
Binary files /dev/null and b/PAR 152/cone_dataset/78.jpg differ
diff --git a/PAR 152/cone_dataset/78.xml b/PAR 152/cone_dataset/78.xml
new file mode 100644
index 0000000000000000000000000000000000000000..ca56d792b10f822085cf171a72295f8a66256072
--- /dev/null
+++ b/PAR 152/cone_dataset/78.xml	
@@ -0,0 +1,38 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>78.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\78.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>337</width>
+		<height>506</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>138</xmin>
+			<ymin>164</ymin>
+			<xmax>306</xmax>
+			<ymax>457</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>53</xmin>
+			<ymin>162</ymin>
+			<xmax>109</xmax>
+			<ymax>252</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/79.jpg b/PAR 152/cone_dataset/79.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..ece385097ff446a46bc08c20c02cb28eca2ad2fc
Binary files /dev/null and b/PAR 152/cone_dataset/79.jpg differ
diff --git a/PAR 152/cone_dataset/79.xml b/PAR 152/cone_dataset/79.xml
new file mode 100644
index 0000000000000000000000000000000000000000..438ff7841bfccfdf4a44f9c9ffac90bf7f0c3bd8
--- /dev/null
+++ b/PAR 152/cone_dataset/79.xml	
@@ -0,0 +1,62 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>79.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\79.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>505</width>
+		<height>339</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>317</xmin>
+			<ymin>106</ymin>
+			<xmax>435</xmax>
+			<ymax>319</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>267</xmin>
+			<ymin>140</ymin>
+			<xmax>340</xmax>
+			<ymax>266</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>239</xmin>
+			<ymin>167</ymin>
+			<xmax>272</xmax>
+			<ymax>233</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>419</xmin>
+			<ymin>164</ymin>
+			<xmax>470</xmax>
+			<ymax>253</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/8.jpg b/PAR 152/cone_dataset/8.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..ddbb595eb4a93591f923c97270bbfbbc9f2a9a35
Binary files /dev/null and b/PAR 152/cone_dataset/8.jpg differ
diff --git a/PAR 152/cone_dataset/8.xml b/PAR 152/cone_dataset/8.xml
new file mode 100644
index 0000000000000000000000000000000000000000..f0bfc8c54c84f3fe60fafb5dc905943ff5940370
--- /dev/null
+++ b/PAR 152/cone_dataset/8.xml	
@@ -0,0 +1,74 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>8.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\8.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>507</width>
+		<height>338</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>332</xmin>
+			<ymin>52</ymin>
+			<xmax>460</xmax>
+			<ymax>295</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>207</xmin>
+			<ymin>131</ymin>
+			<xmax>242</xmax>
+			<ymax>205</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>169</xmin>
+			<ymin>147</ymin>
+			<xmax>192</xmax>
+			<ymax>191</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>155</xmin>
+			<ymin>155</ymin>
+			<xmax>169</xmax>
+			<ymax>183</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>47</xmin>
+			<ymin>167</ymin>
+			<xmax>52</xmax>
+			<ymax>174</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/80.jpg b/PAR 152/cone_dataset/80.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..169efd966d71d1069cba2818faff14482d8acdf4
Binary files /dev/null and b/PAR 152/cone_dataset/80.jpg differ
diff --git a/PAR 152/cone_dataset/80.xml b/PAR 152/cone_dataset/80.xml
new file mode 100644
index 0000000000000000000000000000000000000000..6fda51997bc8d2cea21b50c137b24bca933c66f8
--- /dev/null
+++ b/PAR 152/cone_dataset/80.xml	
@@ -0,0 +1,62 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>80.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\80.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>366</width>
+		<height>467</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>98</xmin>
+			<ymin>211</ymin>
+			<xmax>182</xmax>
+			<ymax>357</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>99</xmin>
+			<ymin>202</ymin>
+			<xmax>148</xmax>
+			<ymax>299</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>134</xmin>
+			<ymin>241</ymin>
+			<xmax>228</xmax>
+			<ymax>465</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>108</xmin>
+			<ymin>195</ymin>
+			<xmax>133</xmax>
+			<ymax>249</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/81.jpg b/PAR 152/cone_dataset/81.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..a4a3efbeec838e054a4bbf4a041fdbd90cb9f417
Binary files /dev/null and b/PAR 152/cone_dataset/81.jpg differ
diff --git a/PAR 152/cone_dataset/81.xml b/PAR 152/cone_dataset/81.xml
new file mode 100644
index 0000000000000000000000000000000000000000..0c4bb3847fb79e9c08f6ecca23529f971d20e66e
--- /dev/null
+++ b/PAR 152/cone_dataset/81.xml	
@@ -0,0 +1,62 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>81.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\81.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>414</width>
+		<height>414</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>78</xmin>
+			<ymin>55</ymin>
+			<xmax>260</xmax>
+			<ymax>308</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>257</xmin>
+			<ymin>121</ymin>
+			<xmax>356</xmax>
+			<ymax>272</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>233</xmin>
+			<ymin>180</ymin>
+			<xmax>278</xmax>
+			<ymax>247</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>43</xmin>
+			<ymin>160</ymin>
+			<xmax>123</xmax>
+			<ymax>253</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/82.jpg b/PAR 152/cone_dataset/82.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..eb62e66ad07dd40e16e197f2888339f684f1c72d
Binary files /dev/null and b/PAR 152/cone_dataset/82.jpg differ
diff --git a/PAR 152/cone_dataset/82.xml b/PAR 152/cone_dataset/82.xml
new file mode 100644
index 0000000000000000000000000000000000000000..08e829f79ce8bd927ea31c698b72a3efa73b7ffa
--- /dev/null
+++ b/PAR 152/cone_dataset/82.xml	
@@ -0,0 +1,62 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>82.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\82.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>514</width>
+		<height>333</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>105</xmin>
+			<ymin>33</ymin>
+			<xmax>191</xmax>
+			<ymax>163</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>13</xmin>
+			<ymin>25</ymin>
+			<xmax>83</xmax>
+			<ymax>136</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>376</xmin>
+			<ymin>90</ymin>
+			<xmax>506</xmax>
+			<ymax>292</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>205</xmin>
+			<ymin>168</ymin>
+			<xmax>363</xmax>
+			<ymax>296</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/83.jpg b/PAR 152/cone_dataset/83.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..7a255f822981028fbc5df1becf05a10c2df95d60
Binary files /dev/null and b/PAR 152/cone_dataset/83.jpg differ
diff --git a/PAR 152/cone_dataset/83.xml b/PAR 152/cone_dataset/83.xml
new file mode 100644
index 0000000000000000000000000000000000000000..1797700a59aab71846ac7cf94a08535dfd379470
--- /dev/null
+++ b/PAR 152/cone_dataset/83.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>83.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\83.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>509</width>
+		<height>339</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>252</xmin>
+			<ymin>1</ymin>
+			<xmax>509</xmax>
+			<ymax>336</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/84.jpg b/PAR 152/cone_dataset/84.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..d466eac9ac903144d8515cfa3958e2727d9853ac
Binary files /dev/null and b/PAR 152/cone_dataset/84.jpg differ
diff --git a/PAR 152/cone_dataset/84.xml b/PAR 152/cone_dataset/84.xml
new file mode 100644
index 0000000000000000000000000000000000000000..b55dd0d3ef3f0e9d7c24aa9be7b8417fb256779d
--- /dev/null
+++ b/PAR 152/cone_dataset/84.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>84.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\84.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>509</width>
+		<height>339</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>192</xmin>
+			<ymin>189</ymin>
+			<xmax>333</xmax>
+			<ymax>325</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/85.jpg b/PAR 152/cone_dataset/85.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..710f6b99d04e7999f40c2604335368d870ca9fed
Binary files /dev/null and b/PAR 152/cone_dataset/85.jpg differ
diff --git a/PAR 152/cone_dataset/85.xml b/PAR 152/cone_dataset/85.xml
new file mode 100644
index 0000000000000000000000000000000000000000..2b14df2b422c4309989d3315e635809905275f8e
--- /dev/null
+++ b/PAR 152/cone_dataset/85.xml	
@@ -0,0 +1,62 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>85.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\85.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>509</width>
+		<height>339</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>146</xmin>
+			<ymin>71</ymin>
+			<xmax>228</xmax>
+			<ymax>215</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>294</xmin>
+			<ymin>28</ymin>
+			<xmax>407</xmax>
+			<ymax>187</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>38</xmin>
+			<ymin>109</ymin>
+			<xmax>114</xmax>
+			<ymax>232</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>408</xmin>
+			<ymin>73</ymin>
+			<xmax>497</xmax>
+			<ymax>222</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/86.jpg b/PAR 152/cone_dataset/86.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..9556b6df8396bb1c8197bca623854c38893bd437
Binary files /dev/null and b/PAR 152/cone_dataset/86.jpg differ
diff --git a/PAR 152/cone_dataset/86.xml b/PAR 152/cone_dataset/86.xml
new file mode 100644
index 0000000000000000000000000000000000000000..1c4e504ab35cd39b5812c65a351236812066c7ef
--- /dev/null
+++ b/PAR 152/cone_dataset/86.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>86.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\86.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>507</width>
+		<height>338</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>193</xmin>
+			<ymin>12</ymin>
+			<xmax>457</xmax>
+			<ymax>275</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/87.jpg b/PAR 152/cone_dataset/87.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..fba51bade931d2fdff72e4ae76544e8ebf4f89f5
Binary files /dev/null and b/PAR 152/cone_dataset/87.jpg differ
diff --git a/PAR 152/cone_dataset/87.xml b/PAR 152/cone_dataset/87.xml
new file mode 100644
index 0000000000000000000000000000000000000000..0648a0c8b0f96d2381e2cc2b3fd1904ce05da10f
--- /dev/null
+++ b/PAR 152/cone_dataset/87.xml	
@@ -0,0 +1,62 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>87.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\87.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>339</width>
+		<height>506</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>159</xmin>
+			<ymin>223</ymin>
+			<xmax>287</xmax>
+			<ymax>454</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>39</xmin>
+			<ymin>40</ymin>
+			<xmax>77</xmax>
+			<ymax>91</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>114</xmin>
+			<ymin>5</ymin>
+			<xmax>143</xmax>
+			<ymax>43</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>165</xmin>
+			<ymin>4</ymin>
+			<xmax>178</xmax>
+			<ymax>25</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/88.jpg b/PAR 152/cone_dataset/88.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..1c6bbd2fde51c2b43fcc08de769a93ed3d632152
Binary files /dev/null and b/PAR 152/cone_dataset/88.jpg differ
diff --git a/PAR 152/cone_dataset/88.xml b/PAR 152/cone_dataset/88.xml
new file mode 100644
index 0000000000000000000000000000000000000000..f61101ba72552659d6c4480315f3c96bf656c086
--- /dev/null
+++ b/PAR 152/cone_dataset/88.xml	
@@ -0,0 +1,62 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>88.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\88.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>478</width>
+		<height>359</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>1</xmin>
+			<ymin>52</ymin>
+			<xmax>106</xmax>
+			<ymax>355</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>204</xmin>
+			<ymin>125</ymin>
+			<xmax>234</xmax>
+			<ymax>193</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>231</xmin>
+			<ymin>139</ymin>
+			<xmax>257</xmax>
+			<ymax>180</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>249</xmin>
+			<ymin>145</ymin>
+			<xmax>261</xmax>
+			<ymax>170</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/89.jpg b/PAR 152/cone_dataset/89.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..f4ac7318edb7e8b07d4cc4055d21b9d8040ce91b
Binary files /dev/null and b/PAR 152/cone_dataset/89.jpg differ
diff --git a/PAR 152/cone_dataset/89.xml b/PAR 152/cone_dataset/89.xml
new file mode 100644
index 0000000000000000000000000000000000000000..b953d1ecb6ff011c9f2ade1f3ac49ab87e56a7cd
--- /dev/null
+++ b/PAR 152/cone_dataset/89.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>89.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\89.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>338</width>
+		<height>507</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>62</xmin>
+			<ymin>10</ymin>
+			<xmax>291</xmax>
+			<ymax>492</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/9.jpg b/PAR 152/cone_dataset/9.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..5ea3555212980a9a5e49999f6e94c863ba876f05
Binary files /dev/null and b/PAR 152/cone_dataset/9.jpg differ
diff --git a/PAR 152/cone_dataset/9.xml b/PAR 152/cone_dataset/9.xml
new file mode 100644
index 0000000000000000000000000000000000000000..fd785042d0bb01466c4f88da84cf1199d672ef50
--- /dev/null
+++ b/PAR 152/cone_dataset/9.xml	
@@ -0,0 +1,50 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>9.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\9.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>507</width>
+		<height>338</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>359</xmin>
+			<ymin>94</ymin>
+			<xmax>484</xmax>
+			<ymax>293</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>276</xmin>
+			<ymin>104</ymin>
+			<xmax>362</xmax>
+			<ymax>257</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>120</xmin>
+			<ymin>168</ymin>
+			<xmax>302</xmax>
+			<ymax>293</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/90.jpg b/PAR 152/cone_dataset/90.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..18b17914904fc0e94e8f8109496f700d92503a56
Binary files /dev/null and b/PAR 152/cone_dataset/90.jpg differ
diff --git a/PAR 152/cone_dataset/90.xml b/PAR 152/cone_dataset/90.xml
new file mode 100644
index 0000000000000000000000000000000000000000..e40077788f69f2e374503b6d5d0bea7abaec493e
--- /dev/null
+++ b/PAR 152/cone_dataset/90.xml	
@@ -0,0 +1,98 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>90.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\90.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>535</width>
+		<height>322</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>220</xmin>
+			<ymin>203</ymin>
+			<xmax>258</xmax>
+			<ymax>250</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>121</xmin>
+			<ymin>213</ymin>
+			<xmax>135</xmax>
+			<ymax>241</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>70</xmin>
+			<ymin>203</ymin>
+			<xmax>103</xmax>
+			<ymax>244</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>6</xmin>
+			<ymin>213</ymin>
+			<xmax>30</xmax>
+			<ymax>243</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>308</xmin>
+			<ymin>177</ymin>
+			<xmax>359</xmax>
+			<ymax>261</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>396</xmin>
+			<ymin>221</ymin>
+			<xmax>415</xmax>
+			<ymax>241</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>458</xmin>
+			<ymin>224</ymin>
+			<xmax>470</xmax>
+			<ymax>241</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/91.jpg b/PAR 152/cone_dataset/91.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..b8cfdaf0c1eeccad0a85f8e139357f77e1e044ee
Binary files /dev/null and b/PAR 152/cone_dataset/91.jpg differ
diff --git a/PAR 152/cone_dataset/91.xml b/PAR 152/cone_dataset/91.xml
new file mode 100644
index 0000000000000000000000000000000000000000..84472c691496745cc5faab9a0176fa1f32bbff79
--- /dev/null
+++ b/PAR 152/cone_dataset/91.xml	
@@ -0,0 +1,50 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>91.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\91.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>553</width>
+		<height>311</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>148</xmin>
+			<ymin>27</ymin>
+			<xmax>262</xmax>
+			<ymax>161</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>257</xmin>
+			<ymin>1</ymin>
+			<xmax>350</xmax>
+			<ymax>137</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>1</xmin>
+			<ymin>1</ymin>
+			<xmax>122</xmax>
+			<ymax>88</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/92.jpg b/PAR 152/cone_dataset/92.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..aa8b6063eb577fa9456e9252b8d5bd66ce588ad0
Binary files /dev/null and b/PAR 152/cone_dataset/92.jpg differ
diff --git a/PAR 152/cone_dataset/92.xml b/PAR 152/cone_dataset/92.xml
new file mode 100644
index 0000000000000000000000000000000000000000..f7f18b524196b3a3caea2cbb6998a95ee8cf8ad7
--- /dev/null
+++ b/PAR 152/cone_dataset/92.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>92.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\92.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>359</width>
+		<height>479</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>69</xmin>
+			<ymin>190</ymin>
+			<xmax>223</xmax>
+			<ymax>389</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/93.jpg b/PAR 152/cone_dataset/93.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..b9e54e70e62c21919d4fed814a885f47e4f0b4d5
Binary files /dev/null and b/PAR 152/cone_dataset/93.jpg differ
diff --git a/PAR 152/cone_dataset/93.xml b/PAR 152/cone_dataset/93.xml
new file mode 100644
index 0000000000000000000000000000000000000000..3805ae278700271f60d861a76293f88457c2e235
--- /dev/null
+++ b/PAR 152/cone_dataset/93.xml	
@@ -0,0 +1,62 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>93.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\93.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>337</width>
+		<height>507</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>91</xmin>
+			<ymin>39</ymin>
+			<xmax>178</xmax>
+			<ymax>161</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>3</xmin>
+			<ymin>28</ymin>
+			<xmax>76</xmax>
+			<ymax>128</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>223</xmin>
+			<ymin>60</ymin>
+			<xmax>327</xmax>
+			<ymax>210</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>259</xmin>
+			<ymin>4</ymin>
+			<xmax>290</xmax>
+			<ymax>53</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/94.jpg b/PAR 152/cone_dataset/94.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..98a8d510dd4ddd4de3aa82558e83072c74b1dfed
Binary files /dev/null and b/PAR 152/cone_dataset/94.jpg differ
diff --git a/PAR 152/cone_dataset/94.xml b/PAR 152/cone_dataset/94.xml
new file mode 100644
index 0000000000000000000000000000000000000000..7337dca0b4b3370976e3e7227bb8778a8b687e5f
--- /dev/null
+++ b/PAR 152/cone_dataset/94.xml	
@@ -0,0 +1,62 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>94.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\94.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>507</width>
+		<height>339</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>218</xmin>
+			<ymin>24</ymin>
+			<xmax>397</xmax>
+			<ymax>294</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>130</xmin>
+			<ymin>100</ymin>
+			<xmax>183</xmax>
+			<ymax>188</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>111</xmin>
+			<ymin>120</ymin>
+			<xmax>138</xmax>
+			<ymax>167</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>101</xmin>
+			<ymin>125</ymin>
+			<xmax>122</xmax>
+			<ymax>156</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/95.jpg b/PAR 152/cone_dataset/95.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..eed8aac79f979ff1b531ece250df243e668d6cc6
Binary files /dev/null and b/PAR 152/cone_dataset/95.jpg differ
diff --git a/PAR 152/cone_dataset/95.xml b/PAR 152/cone_dataset/95.xml
new file mode 100644
index 0000000000000000000000000000000000000000..c29900471df0f112799cada8017beefbc06bb433
--- /dev/null
+++ b/PAR 152/cone_dataset/95.xml	
@@ -0,0 +1,110 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>95.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\95.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>510</width>
+		<height>337</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>312</xmin>
+			<ymin>168</ymin>
+			<xmax>402</xmax>
+			<ymax>325</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>430</xmin>
+			<ymin>23</ymin>
+			<xmax>462</xmax>
+			<ymax>75</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>366</xmin>
+			<ymin>22</ymin>
+			<xmax>399</xmax>
+			<ymax>77</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>311</xmin>
+			<ymin>23</ymin>
+			<xmax>341</xmax>
+			<ymax>75</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>292</xmin>
+			<ymin>117</ymin>
+			<xmax>366</xmax>
+			<ymax>248</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>280</xmin>
+			<ymin>92</ymin>
+			<xmax>333</xmax>
+			<ymax>202</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>121</xmin>
+			<ymin>16</ymin>
+			<xmax>155</xmax>
+			<ymax>65</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>65</xmin>
+			<ymin>16</ymin>
+			<xmax>106</xmax>
+			<ymax>66</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/96.jpg b/PAR 152/cone_dataset/96.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..92f76386f69f98b22d2f1b83e534a5e0e307dd56
Binary files /dev/null and b/PAR 152/cone_dataset/96.jpg differ
diff --git a/PAR 152/cone_dataset/96.xml b/PAR 152/cone_dataset/96.xml
new file mode 100644
index 0000000000000000000000000000000000000000..23a5390244b0c27ab51f5126142e283bd16b3850
--- /dev/null
+++ b/PAR 152/cone_dataset/96.xml	
@@ -0,0 +1,62 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>96.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\96.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>505</width>
+		<height>342</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>317</xmin>
+			<ymin>113</ymin>
+			<xmax>373</xmax>
+			<ymax>171</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>189</xmin>
+			<ymin>94</ymin>
+			<xmax>255</xmax>
+			<ymax>176</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>218</xmin>
+			<ymin>183</ymin>
+			<xmax>421</xmax>
+			<ymax>320</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>263</xmin>
+			<ymin>96</ymin>
+			<xmax>289</xmax>
+			<ymax>137</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/97.jpg b/PAR 152/cone_dataset/97.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..a8e9cdc28f8f8d7852f781f913b0adfc3fda53ce
Binary files /dev/null and b/PAR 152/cone_dataset/97.jpg differ
diff --git a/PAR 152/cone_dataset/97.xml b/PAR 152/cone_dataset/97.xml
new file mode 100644
index 0000000000000000000000000000000000000000..3c2c10de8201fd621b1d60bd819d4c7c097c8296
--- /dev/null
+++ b/PAR 152/cone_dataset/97.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>97.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\97.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>432</width>
+		<height>400</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>97</xmin>
+			<ymin>1</ymin>
+			<xmax>166</xmax>
+			<ymax>108</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/98.jpg b/PAR 152/cone_dataset/98.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..e03c9664414ee18aec9e61db6d64d80e2bb6c3a2
Binary files /dev/null and b/PAR 152/cone_dataset/98.jpg differ
diff --git a/PAR 152/cone_dataset/98.xml b/PAR 152/cone_dataset/98.xml
new file mode 100644
index 0000000000000000000000000000000000000000..8e096381babb4e30abbbb42573548353f5cfa28d
--- /dev/null
+++ b/PAR 152/cone_dataset/98.xml	
@@ -0,0 +1,26 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>98.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\98.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>509</width>
+		<height>339</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>179</xmin>
+			<ymin>43</ymin>
+			<xmax>255</xmax>
+			<ymax>167</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/PAR 152/cone_dataset/99.jpg b/PAR 152/cone_dataset/99.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..7a810e26f4e31ca7dcac781532854e5ca0e3c112
Binary files /dev/null and b/PAR 152/cone_dataset/99.jpg differ
diff --git a/PAR 152/cone_dataset/99.xml b/PAR 152/cone_dataset/99.xml
new file mode 100644
index 0000000000000000000000000000000000000000..fcae8467e04e5d6e324cd8b075ab9e0affe9c2e5
--- /dev/null
+++ b/PAR 152/cone_dataset/99.xml	
@@ -0,0 +1,86 @@
+<annotation>
+	<folder>cone_dataset</folder>
+	<filename>99.jpg</filename>
+	<path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\99.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>476</width>
+		<height>362</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>201</xmin>
+			<ymin>170</ymin>
+			<xmax>294</xmax>
+			<ymax>330</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>231</xmin>
+			<ymin>156</ymin>
+			<xmax>306</xmax>
+			<ymax>297</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>266</xmin>
+			<ymin>139</ymin>
+			<xmax>342</xmax>
+			<ymax>265</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>308</xmin>
+			<ymin>128</ymin>
+			<xmax>370</xmax>
+			<ymax>241</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>328</xmin>
+			<ymin>117</ymin>
+			<xmax>388</xmax>
+			<ymax>217</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>cone</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>372</xmin>
+			<ymin>101</ymin>
+			<xmax>421</xmax>
+			<ymax>186</ymax>
+		</bndbox>
+	</object>
+</annotation>