README.md

Transformer-models-in-CV

some basic transformer models about computer vision.

TMICV

Transformer Models In Computer Vision
Explore the docs »

View Detr Demo · Report Bug · Request Feature

Table of Contents

1. About The Project
   • Built With
2. Getting Started
3. Usage
4. Roadmap
5. Contributing
6. License
7. Contact
8. Acknowledgments

About The Project

Here's my project of transformers mudules in computer vision. I have inplemented the projects below:

Backbone	Models	Demo for Inference
✅ Resnet ✅ Densenet ✅ ResNext	✅ Vision Transformer ✅ Transformer Encoder ✅ Transfomer Decoder ✅ Swin Transformer ✅ DETR	✅ Tiny DETR ✅ Swin Transformer ✅ DETR

Under Construction:

• 🚧 DETR
• 🚧 MAE

To Do In the future:

• 🕑 Swin Transformer
• 🕑 MAE
• 🕑 DEiT

(back to top)

Built With Papers

• Deep Residual Learning for Image Recognition
• Densely Connected Convolutional Networks
• Attention Is All You Need
• An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
• End-to-End Object Detection with Transformers
• Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
• Masked Autoencoders Are Scalable Vision Learners

• pytorch

(back to top)

Getting Started

1. 👋Give the project a star⭐!
2. 🔥Clone the repo.

   git clone https://github.com/LRY89757/Transformer-models-in-CV.git

3. 💼install relevant packages:

   pip install -r requirements.txt

Prerequisites

This is an example of how to list things you need to use the software and how to install them.

• requirements

  pip install -r requirements.txt

Contributing

Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement". Don't forget to give the project a star! Thanks again!

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

(back to top)

License

Distributed under the MIT License. See LICENSE.txt for more information.

(back to top)

Contact

My Name - @lry89757 - lry89757@gmail.com

Project Link: https://github.com/LRY89757/Transformer-models-in-CV

(back to top)