This repo is the official implementation of DPT: Deformable Patch-based Transformer for Visual Recognition (ACM MM2021). We provide code and models for the following tasks:
Image Classification: Detailed instruction and information see classification/README.md.
Object Detection: Detailed instruction and information see detection/README.md.
The papar has been relased on [Arxiv].
Deformable Patch (DePatch) is a plug-and-play module. It learns to adaptively split the images input patches with different positions and scales in a data-driven way, rather than using predefined fixed patches. In this way, our method can well preserve the semantics in patches.
In this repository, code and models for a Deformable Patch-based Transformer (DPT) are provided. As this field is developing rapidly, we are willing to see our DePatch applied to some other latest architectures and promote further research.
Training commands and pretrained models are provided >>> here <<<.
| Method | #Params (M) | FLOPs(G) | Acc@1 |
|---|---|---|---|
| DPT-Tiny | 15.2 | 2.1 | 77.4 |
| DPT-Small | 26.4 | 4.0 | 81.0 |
| DPT-Medium | 46.1 | 6.9 | 81.9 |
Training command and detailed results are provided >>> here <<<.
@inproceedings{chenDPT21,
title = {DPT: Deformable Patch-based Transformer for Visual Recognition},
author = {Zhiyang Chen and Yousong Zhu and Chaoyang Zhao and Guosheng Hu and Wei Zeng and Jinqiao Wang and Ming Tang},
booktitle={Proceedings of the ACM International Conference on Multimedia},
year={2021}
}
This repository is released under the Apache 2.0 license as found in the LICENSE file.
Our implementation is mainly based on PVT. The CUDA operator is borrowed from Deformable-DETR. You may refer these repositories for further information.