Swin Transformers architecture

Overview

##swin-v2 and implementaion

This repository contains an implementation of the Swin Transformer (first version) network using TensorFlow. Swin Transformers are a type of Vision Transformer (ViT) that have demonstrated strong performance on various computer vision tasks, particularly for hierarchical image representation.

Table of Contents

• Installation
• Usage
• Project Structure
• License
• Acknowledgements

Installation and use the code

To get started with the Swin Transformers, clone this repository and install the necessary dependencies:

git clone https://github.com/YShokrollahi/swin-transformers.git
cd swin-transformers
pip install -r requirements.txt

Make sure you have TensorFlow installed. You can install TensorFlow using pip:

pip install tensorflow

Usage

You can find an example of how to use the Swin Transformer network in the SwinTransformers.ipynb Jupyter notebook. This notebook demonstrates how to train and evaluate the Swin Transformer on the CIFAR-10 dataset.

To run the notebook, execute the following command in your terminal:

jupyter notebook SwinTransformers.ipynb

Project Structure

swin-transformers/
├── scripts/
│   └── __init__.py
├── .gitattributes
├── LICENSE
├── README.md
├── SwinTransformers.ipynb

• scripts/: Directory containing utility scripts and modules.
• .gitattributes: Git configuration file.
• LICENSE: MIT license file.
• README.md: Project documentation file.
• SwinTransformers.ipynb: Jupyter notebook with example usage.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgements

• The implementation is inspired by the original Swin Transformer paper.
• Thanks to the TensorFlow community for their support and contributions.