This repository contains the code and resources for replicating the Vision Transformer (ViT) architecture, a deep learning model that has shown remarkable performance in computer vision tasks.
The Vision Transformer (ViT) is a neural network architecture that applies the principles of the Transformer architecture, originally designed for natural language processing, to computer vision tasks. ViT has shown competitive performance on image classification tasks and is known for its simplicity and scalability.
This project aims to replicate the Vision Transformer (ViT) paper titled "An Image Is Worth 16x16 Words: Transformers for Image Recognition at Scale" using PyTorch, providing a complete codebase to train and evaluate the model on standard image classification datasets.
You can access the paper using the following link: ViT Paper. This paper provides in-depth information about the ViT architecture, its applications, and experimental results.
The official GitHub repository for the Vision Transformer (ViT) implementation by Google Research can be found at: ViT GitHub Repository. This repository contains the source code, pre-trained models, and resources related to the Vision Transformer.
If you use this code or replicate the results, please consider citing the original paper:
@article{dosovitskiy2020vit,
title={An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale},
author={Dosovitskiy, Alexey and Beyer, Lucas and Kolesnikov, Alexander and Weissenborn, Dirk and Zhai, Xiaohua and Unterthiner, Thomas and Dehghani, Mostafa and Minderer, Matthias and Heigold, Georg and Gelly, Sylvain and Uszkoreit, Jakob and Houlsby, Neil},
journal={ICLR},
year={2021}
}Contributions to this replication project are welcome. Whether you have suggestions, improvements, or new findings, please feel free to submit issues and pull requests. Collaborative efforts will help in achieving a successful replication.
This project is licensed under the MIT License - see the LICENSE file for details.