This repository contains the implementation of the Vision Transformer (ViT) model, a state-of-the-art deep learning architecture for image classification tasks.The Vision Transformer is a state-of-the-art deep learning architecture that leverages the Transformer model's self-attention mechanism to achieve remarkable performance on various computer vision benchmarks.
Vision Transformer Architecture: Complete implementation of the Vision Transformer model, comprising self-attention layers and feed-forward neural networks specifically designed for image classification tasks.
Documentation and Examples: Comprehensive documentation and example scripts to guide users through implementation details, model configuration, and usage of different components.
- Clone this repository:
git clone https://github.com/asadimtiazmalik/ViT-Implementation.git cd vision-transformer
- Installing dependencies:
pip install -r requirements.txt
- The vit.py file contains the implementation of the Vision Transformer model.
- The vit.ipynb notebook provides an example usage of the Vision Transformer for image classification. It includes data loading, model training, evaluation, and visualization.
- The implementation of the Vision Transformer model is based on the following paper: Dosovitskiy, A., et al. (2020). "An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale."