Vision Transformer (ViT) for Image Classification

Description

This project implements Vision Transformer (ViT) for image classification. Unlike CNNs, ViT splits images into patches and processes them as sequences using transformer architecture. It includes patch embedding, positional encoding, and multi-head self-attention layers, achieving state-of-the-art results on image classification tasks.

Features

Core Features

• Vision Transformer (ViT) architecture
• Patch-based image embedding
• Multi-head self-attention mechanism
• Positional encoding for spatial information
• State-of-the-art classification accuracy

Additional Features

• Data Preparation: Automated data splitting and organization
• Advanced Augmentation: MixUp, CutMix, Random Erasing, AutoAugment
• Visualization Tools: Attention maps, patch visualization, training curves
• Evaluation Metrics: Comprehensive evaluation with confusion matrix, per-class accuracy
• Model Comparison: Compare different ViT configurations
• Testing Suite: Unit tests for all model components
• Easy Setup: Automated environment setup script

Technologies

• Python
• PyTorch
• TensorFlow
• Vision Transformer (ViT)
• Patch Embedding
• Self-Attention
• Transformers
• Jupyter Notebook

Installation

pip install -r requirements.txt

Usage

Training

python train.py --config config.yaml

Inference

python inference.py --model_path models/vit_model.pth --image_path path/to/image.jpg

Jupyter Notebook

Open vit_image_classification.ipynb for interactive exploration.

Data Preparation

# Create directory structure
python data_preparation.py --action create_structure

# Split data into train/val/test
python data_preparation.py --action split --source_dir your_data --target_dir data

# Get dataset statistics
python data_preparation.py --action stats --target_dir data/train

Evaluation

python evaluate.py --model_path models/best_model.pth --config config.yaml --plot

Model Comparison

python model_comparison.py

Testing

python test_model.py

Setup

python setup.py

Project Structure

vit-image-classification/
├── README.md                          # Project documentation
├── requirements.txt                   # Python dependencies
├── config.yaml                        # Configuration file
├── LICENSE                            # MIT License
├── .gitignore                         # Git ignore file
├── DATA_GUIDE.md                      # Data preparation guide
├── PROJECT_INFO.md                    # Project information
│
├── Core Model Files
├── vit_model.py                       # ViT model implementation
├── train.py                           # Training script
├── inference.py                       # Inference script
├── utils.py                           # Utility functions
│
├── Data & Augmentation
├── data_preparation.py                # Data preparation utilities
├── augmentation.py                    # Advanced augmentation
│
├── Evaluation & Analysis
├── evaluate.py                         # Evaluation metrics
├── model_comparison.py                # Model comparison tools
├── visualization.py                   # Visualization tools
│
├── Testing & Setup
├── test_model.py                      # Unit tests
├── setup.py                           # Setup script
├── example_usage.py                    # Example usage
│
├── Notebooks
├── vit_image_classification.ipynb     # Jupyter notebook
│
└── Directories (created during setup)
    ├── data/                          # Training data
    ├── models/                        # Saved models
    ├── logs/                          # Training logs
    └── evaluation_results/            # Evaluation outputs

Author

RSK World

• Website: https://rskworld.in
• Email: help@rskworld.in
• Phone: +91 93305 39277

License

This project is provided as-is for educational and research purposes.