CIFAR-10 Image Classification

This project implements deep learning models for image classification on the CIFAR-10 dataset. It provides a complete pipeline for training, evaluating, and visualizing the performance of different CNN architectures.

Project Overview

The CIFAR-10 dataset consists of 60,000 32x32 color images in 10 classes, with 6,000 images per class. This project offers:

• Training of custom CNN architectures from scratch
• Transfer learning using pre-trained ResNet50
• Comprehensive evaluation metrics and visualizations
• Modular codebase for easy experimentation

Project Structure

CIFAR/
│
├── main.py                   # Entry point for training and evaluation
├── README.md                 # This file
│
└── src/
    ├── data_processing/      # Data loading and augmentation
    │   ├── augment.py        # Data augmentation functions
    │   └── utils.py          # Data utilities
    │
    ├── networks/             # Model architectures
    │   ├── classic_network.py # Custom CNN architecture
    │   ├── transfer_network.py # Transfer learning with ResNet50
    │   └── utils.py          # Network utilities
    │
    ├── training/             # Training functionality
    │   ├── trainer.py        # Training loop implementation
    │   └── utils.py          # Training utilities
    │
    └── evaluation/           # Evaluation functionality
        ├── evaluate.py       # Model evaluation
        ├── utils.py          # Evaluation utilities
        └── visualize.py      # Visualization functions

Usage

Training a Model

To train a model from scratch:

python main.py --model classic --epochs 30 --batch_size 128 --lr 0.001 --gpu

To train using transfer learning with ResNet50:

python main.py --model transfer --epochs 20 --batch_size 64 --lr 0.0001 --gpu

Command Line Arguments

• --model: Model architecture to use (classic or transfer)
• --epochs: Number of training epochs (default: 30)
• --batch_size: Batch size for training (default: 128)
• --lr: Learning rate (default: 0.001)
• --weight_decay: Weight decay for optimizer (default: 1e-4)
• --seed: Random seed (default: 42)
• --gpu: Use GPU if available (flag)
• --evaluate_only: Only run evaluation on a trained model (flag)

Evaluation Only

To evaluate a trained model without retraining:

python main.py --model classic --evaluate_only --gpu

Features

Data Augmentation

The project implements several data augmentation techniques:

• Random cropping
• Random horizontal flips
• Random rotation
• Color jitter

Model Architectures

1. ClassicCNN: A custom CNN architecture with:

   • 4 convolutional blocks with increasing filter sizes
   • Batch normalization
   • Max pooling
   • Dropout for regularization
   • Fully connected layers

2. TransferResNet50: A transfer learning approach using:

   • Pre-trained ResNet50 as feature extractor
   • Custom classification head for CIFAR-10

Evaluation Metrics

• Accuracy (overall and per-class)
• Precision, recall, and F1 score
• Confusion matrix
• Feature embeddings visualization (t-SNE and PCA)
• Visualization of misclassified samples
• Training and validation curves

Output

The results are saved in an output/session_X directory, where X is the session number. Each session directory contains:

• checkpoints/: Model weights for each epoch and the best model
• plots/: Visualization plots (confusion matrices, embeddings, etc.)
• test_results_*.txt: Detailed evaluation metrics
• training_summary.txt: Summary of the training process
• stats_*.json: Training statistics for plotting

Requirements

• Python 3.6+
• PyTorch
• torchvision
• numpy
• matplotlib
• scikit-learn
• tqdm
• pytorch-lightning

License

MIT License