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    TD 1 : Image Classification

    MOD 4.6 Deep Learning & Artificial Intelligence: an introduction

    Introduction

    The objective of this tutorial is to write a complete image classification program in Python. Two classification models will be successively developed and tested: k-nearest neighbors (KNN) and neural networks (NN).

    Description

    This project contains several files and directory. A brief description of each is given.

    • data/: This directory contains the raw dataset, downloaded from the CIFAR-10 website. This directory should contain the folder cifar-10-batches-py/, found within the tar file downloadable from the website. This folder is ignored from source tracking, so it should be created manually.
    • tests/: This folder contains tests for each functionality. The test files are named test_{source-file-to-test}.py, and the functions to test are named test_{function-to-test}.
    • results/: This folder contains some of the results generated by the program.
    • knn.py: Contains functions related to the KNN algorithm.
    • read_cifar.py: Contains functions related to reading and parsing the CIFAR-10 dataset.
    • mlp.py: Contains functions related to the Neural Network algorithm.
    • main.ipynb: Jupyter Notebook containing the main program. It is used to test the algorithms and generate the results. It also contains some descriptions regarding the algorithms, notably a mathematical description of the Neural Network algorithm.

    Usage

    This program was made using the latest version of Python, 3.11.5. This program requires the following packages:

    • numpy==1.26.1
    • notebook==7.0.6
    • scikit-image==0.22.0
    • pytest==7.4.2

    The CIFAR-10 dataset should be downloaded and the extracted contents should be placed a data folder in the root of the project, before any execution.

    To trigger the tests, one should simply run pytest while on the root directory.

    The main program is contained in the main.ipynb file. It can be run using Jupyter Notebook or Jupyter Lab. The results are generated in the results folder.

    References