Edit README.md

README.md

@@ -71,6 +71,10 @@ To enhance image generation and reduce ambiguities between similar digits (e.g.,
 - [DCGAN Tutorial](https://pytorch.org/tutorials/beginner/dcgan_faces_tutorial.html)  
 - [MNIST Dataset](https://pytorch.org/vision/stable/generated/torchvision.datasets.MNIST.html#torchvision.datasets.MNIST)  
 
+Here is the corrected version with only the necessary adjustments:  
+
+---
+
 ## Part 2: Conditional GAN (cGAN) with U-Net  
 
 ### **Generator**  
@@ -88,11 +92,10 @@ The encoder takes a colored picture (3 channels: RGB), processes it through a se
 
 ![architecture Unet](images/unet_architecture.png)  
 
-
 ### **Question:**  
 Knowing that the input and output images have a shape of 256x256 with 3 channels, what will be the dimension of the feature map "x8"?  
 
-**Answer:** The dimension of the feature map x8 is **[numBatch, 512, 1, 1]**.
+**Answer:** The dimension of the feature map x8 is **[numBatch, 512, 32, 32]**.  
 
 ### **Question:**  
 Why are skip connections important in the U-Net architecture?  
@@ -126,40 +129,43 @@ For this project, we use a **70×70 PatchGAN**.
 
 ![patchGAN](images/patchGAN.png)  
 
-question : how many learnable parameters this neural network has ?:
+### **Question:**  
+How many learnable parameters does this neural network have?  
 
-1. conv1:
+1. **conv1:**  
    - Input channels: 6  
    - Output channels: 64  
-- Kernel size: 4*4
-- Parameters in conv1 = (4×4×6+1(bais))×64=6208
+   - Kernel size: 4×4  
+   - Parameters in conv1 = (4×4×6+1(bias))×64 = **6208**  
+
+2. **conv2:**  
+   - Weights: 4 × 4 × 64 × 128 = **131072**  
+   - Biases: **128**  
+   - BatchNorm: (scale + shift) for 128 channels: 2 × 128 = **256**    
+   - Parameters in conv2: **131072 + 128 + 256 = 131456**  
 
-2. conv2:
-- Weights: 4 × 4 × 64 × 128 = 131072
-- Biases: 128
-- BatchNorm: (scale + shift) for 128 channels: 2 × 128 = 256  
-- Parameters in conv2: 131072 + 128 + 256 = 131456
+3. **conv3:**  
+   - Weights: 4 × 4 × 128 × 256 = **524288**  
+   - Biases: **256**  
+   - BatchNorm: (scale + shift) for 256 channels: 2 × 256 = **512**    
+   - Parameters in conv3: **524288 + 256 + 512 = 525056**  
 
-3. conv3:
-- Weights: 4 × 4 × 128 × 256 = 524288
-- Biases: 256
-- BatchNorm: (scale + shift) for 256 channels: 2 × 256 = 512  
-- Parameters in conv3: 524288 + 256 + 512= 525056
+4. **conv4:**  
+   - Weights: 4 × 4 × 256 × 512 = **2097152**  
+   - Biases: **512**  
+   - BatchNorm: (scale + shift) for 512 channels: 2 × 512 = **1024**    
+   - Parameters in conv4: **2097152 + 512 + 1024 = 2098688**  
 
-4. conv4:
-- Weights: 4 × 4 × 256 × 512 = 2097152
-- Biases: 512
-- BatchNorm: (scale + shift) for 512 channels: 2 × 512 = 1024  
-- Parameters in conv4: 2097152+512+1,024=2098688
+5. **out:**  
+   - Weights: 4 × 4 × 512 × 1 = **8192**  
+   - Biases: **1**  
+   - Parameters in out: **8192 + 1 = 8193**  
 
-5. out:
-- Weights: 4 × 4 × 512 × 1 = 8192
-- Biases: 1
-- Parameters in out: 8192 + 1=8193
+**Total Learnable Parameters:**  
 
-**Total Learnable Parameters**
+**6208 + 131456 + 525056 + 2098688 + 8193 = 2,769,601**  
 
-**6,208 + 131,456 + 525,056 + 2,098,688 + 8,193 = 2,769,601**
+---  
 
 ### **Results Comparison: 100 vs. 200 Epochs**  
 
@@ -186,10 +192,10 @@ question : how many learnable parameters this neural network has ?:
 - **Overfitting Issue:** Generalization is poor beyond 100 epochs.  
 - **Limited Dataset Size (378 Images):** Restricts model’s diversity and quality.  
 
-#### Example image of training set at 100 and 200 epochs:
+#### **Example image of training set at 100 and 200 epochs:**  
 ![Example image for training set at 100 and 200 epochs](images/facades_trainingset_100_200_.png)  
 
-#### Example images of evaluation set at 100 and 200 epochs:
+#### **Example images of evaluation set at 100 and 200 epochs:**  
 ![Example images of evaluation set at 100 and 200 epochs](images/facades_valset_100_200_.png)
 
 ## Part 3: Diffusion Models