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CIFAR-10 Computer Vision Models

Project Overview

This repository contains machine learning models developed for a computer vision project using the CIFAR-10 dataset. The project includes two different approaches:

A simple Convolutional Neural Network (CNN) baseline model. An advanced model utilizing transfer learning with ResNet50.

Models

Baseline CNN Model

Description: The baseline model is a simple CNN designed to establish an initial performance benchmark. Performance: The model showed limited accuracy, as indicated by the learning curves and confusion matrix.

ResNet50 Transfer Learning Model

Description: This model employs transfer learning with the ResNet50 architecture, enhanced with additional preprocessing steps. Performance: Significant improvement in accuracy compared to the baseline model, as evidenced by the learning curves and confusion matrix analysis.

R

Setup and Usage

Installation

  • Clone the repository: git clone https://github.com/<your-username>/<your-repo-name>.git
  • Install dependencies: pip install -r requirements.txt

Running the Models

Run main.py to train and evaluate both the baseline CNN and the ResNet50 models:

  • python main.py

This script will handle the following:

  • Load the CIFAR-10 dataset.
  • Train and evaluate the baseline CNN model.
  • Train and evaluate the ResNet50 model.
  • Perform data augmentation for training the models.
  • Generate confusion matrices and ROC curves for performance analysis.

Results

  • The script main.py includes evaluation methods like confusion matrices and ROC curve analysis.
  • Check the output directory for saved model weights and performance metrics.

Contact

Getting Started Prerequisites

Python 3.x
TensorFlow 2.x
OpenCV

Installation

lone the repository: git clone https://github.com/QED137ComputerVisionProject.git

Install the required packages: pip install -r requirements.txt

Running the Models

python3 main.py

Results and Analysis

Learning curves and confusion matrices for each model can be found within their respective directories. A comprehensive analysis and comparison of both models are detailed in the project report, where available. Due to space and computational constraints, the ResNet50 model was trained for only 5 epochs, and its learning curves are presented accordingly. This limitation is primarily due to GPU and CPU resource considerations.