# DAT

**Repository Path**: frankxia2018_admin/DAT

## Basic Information

- **Project Name**: DAT
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2022-01-17
- **Last Updated**: 2022-01-17

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Vision Transformer with Deformable Attention

This repository contains the code for the paper Vision Transformer with Deformable Attention \[[arXiv](https://arxiv.org/abs/2201.00520)\]. 

## Introduction

![Deform_Attn](figures/datt.png)

Deformable attention is proposed to model the relations among tokens effectively under the guidance of the important regions in the feature maps. This flexible scheme enables the self-attention module to focus on relevant regions and capture more informative features. On this basis, we present **Deformable Attention Transformer (DAT)**, a general backbone model with deformable attention for both image classification and other dense prediction tasks.

## Dependencies

- NVIDIA GPU + CUDA 11.1
- Python 3.8 (Recommend to use Anaconda)
- PyTorch == 1.8.0
- timm
- einops
- yacs
- termcolor

## TODO

- [ ] Classification pretrained models.
- [ ] Object Detection codebase & models.
- [ ] Semantic Segmentation codebase & models.
- [ ] CUDA operators to accelerate sampling operations.

## Acknowledgement

This code is developed on the top of [Swin Transformer](https://github.com/microsoft/Swin-Transformer), we thank to their efficient and neat codebase.

## Citation

If you find our work is useful in your research, please consider citing:

```
@misc{xia2022vision,
      title={Vision Transformer with Deformable Attention}, 
      author={Zhuofan Xia and Xuran Pan and Shiji Song and Li Erran Li and Gao Huang},
      year={2022},
      eprint={2201.00520},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}
```

### Contact

[xzf20@mails.tsinghua.edu.cn](xzf20@mails.tsinghua.edu.cn)