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https://github.com/wofmanaf/ResT
This is an official implementation for "ResT: An Efficient Transformer for Visual Recognition".
https://github.com/wofmanaf/ResT
Last synced: about 2 months ago
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This is an official implementation for "ResT: An Efficient Transformer for Visual Recognition".
- Host: GitHub
- URL: https://github.com/wofmanaf/ResT
- Owner: wofmanaf
- License: apache-2.0
- Created: 2021-05-21T13:04:22.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2022-09-28T02:38:39.000Z (about 2 years ago)
- Last Synced: 2023-11-07T14:24:01.686Z (about 1 year ago)
- Language: Python
- Homepage:
- Size: 222 KB
- Stars: 247
- Watchers: 6
- Forks: 28
- Open Issues: 11
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome_vision_transformer - code
- awesome_vision_transformer - code
README
# Updates
- (2022/05/10) Code of [ResTV2](https://arxiv.org/abs/2204.07366) is released! ResTv2 simplifies the EMSA structure in
[ResTv1](https://arxiv.org/abs/2105.13677) (i.e., eliminating the multi-head interaction part) and employs an upsample
operation to reconstruct the lost medium- and high-frequency information caused by the downsampling operation.
# [ResT: An Efficient Transformer for Visual Recognition](https://arxiv.org/abs/2105.13677)
Official PyTorch implementation of **ResTv1** and **ResTv2**, from the following paper:
[ResT: An Efficient Transformer for Visual Recognition](https://arxiv.org/abs/2105.13677). NeurIPS 2021.\
[ResT V2: Simpler, Faster and Stronger](https://arxiv.org/abs/2204.07366). NeurIPS 2022.\
By Qing-Long Zhang and Yu-Bin Yang \
State Key Laboratory for Novel Software Technology at Nanjing University
---
**ResTv1** is initially described in [arxiv](https://arxiv.org/abs/2105.13677), which capably serves as a
general-purpose backbone for computer vision. It can tackle input images with arbitrary size. Besides,
ResT compressed the memory of standard MSA and model the interaction between multi-heads while keeping
the diversity ability.
## Catalog
- [x] ImageNet-1K Training Code
- [x] ImageNet-1K Fine-tuning Code
- [x] Downstream Transfer (Detection, Segmentation) Code
## Results and Pre-trained Models
### ImageNet-1K trained models
| name | resolution |acc@1 | #params | FLOPs | Throughput | model |
|:-----------:|:---:|:---:|:-------:|:-----:|:----------:|:---:|
| ResTv1-Lite | 224x224 | 77.2 | 11M | 1.4G | 1246 | [baidu](https://pan.baidu.com/s/1VVzrzZi_tD3yTp_lw9tU9A)
| ResTv1-S | 224x224 | 79.6 | 14M | 1.9G | 1043 | [baidu](https://pan.baidu.com/s/1Y-MIzzzcQnmrbHfGGR0mrw)
| ResTv1-B | 224x224 | 81.6 | 30M | 4.3G | 673 | [baidu](https://pan.baidu.com/s/1HhR9YxtGIhouZ0GEA4LYlw)
| ResTv1-L | 224x224 | 83.6 | 52M | 7.9G | 429 | [baidu](https://pan.baidu.com/s/14c4u_oRoBcKOt1aTlsBBpw)
| ResTv2-T | 224x224 | 82.3 | 30M | 4.1G | 826 | [baidu](https://pan.baidu.com/s/1LHAbsrXnGsjvAE3d5zhaHQ) |
| ResTv2-T | 384x384 | 83.7 | 30M | 12.7G | 319 | [baidu](https://pan.baidu.com/s/1fEMs_OrDa_xF7Cw1DiBU9w) |
| ResTv2-S | 224x224 | 83.2 | 41M | 6.0G | 687 | [baidu](https://pan.baidu.com/s/1nysV5MTtwsDLChrRa7vmZQ) |
| ResTv2-S | 384x384 | 84.5 | 41M | 18.4G | 256 | [baidu](https://pan.baidu.com/s/1S1GERP-lYEJANYr17xk3dA) |
| ResTv2-B | 224x224 | 83.7 | 56M | 7.9G | 582 | [baidu](https://pan.baidu.com/s/1GH3N2_rbZx816mN87UzYgQ) |
| ResTv2-B | 384x384 | 85.1 | 56M | 24.3G | 210 | [baidu](https://pan.baidu.com/s/12RBMZmf6IlJIB3lIkeBH9Q) |
| ResTv2-L | 224x224 | 84.2 | 87M | 13.8G | 415 | [baidu](https://pan.baidu.com/s/1A2huwk_Ii4ZzQllg1iHrEw) |
| ResTv2-L | 384x384 | 85.4 | 87M | 42.4G | 141 | [baidu](https://pan.baidu.com/s/1dlxiWexb9mho63WdWS8nXg) |
Note: Access code for `baidu` is `rest`. Pretrained models of ResTv1 is now available in [google drive](https://drive.google.com/drive/folders/1H6QUZsKYbU6LECtxzGHKqEeGbx1E8uQ9).
## Installation
Please check [INSTALL.md](INSTALL.md) for installation instructions.
## Evaluation
We give an example evaluation command for a ImageNet-1K pre-trained, then ImageNet-1K fine-tuned ResTv2-T:
Single-GPU
```
python main.py --model restv2_tiny --eval true \
--resume restv2_tiny_384.pth \
--input_size 384 --drop_path 0.1 \
--data_path /path/to/imagenet-1k
```
This should give
```
* Acc@1 83.708 Acc@5 96.524 loss 0.777
```
- For evaluating other model variants, change `--model`, `--resume`, `--input_size` accordingly. You can get the url to pre-trained models from the tables above.
- Setting model-specific `--drop_path` is not strictly required in evaluation, as the `DropPath` module in timm behaves the same during evaluation; but it is required in training. See [TRAINING.md](TRAINING.md) or our paper for the values used for different models.
## Training
See [TRAINING.md](TRAINING.md) for training and fine-tuning instructions.
## Acknowledgement
This repository is built using the [timm](https://github.com/rwightman/pytorch-image-models) library.
## License
This project is released under the Apache License 2.0. Please see the [LICENSE](LICENSE) file for more information.
## Citation
If you find this repository helpful, please consider citing:
**ResTv1**
```
@inproceedings{zhang2021rest,
title={ResT: An Efficient Transformer for Visual Recognition},
author={Qinglong Zhang and Yu-bin Yang},
booktitle={Advances in Neural Information Processing Systems},
year={2021},
url={https://openreview.net/forum?id=6Ab68Ip4Mu}
}
```
**ResTv2**
```
@article{zhang2022rest,
title={ResT V2: Simpler, Faster and Stronger},
author={Zhang, Qing-Long and Yang, Yu-Bin},
journal={arXiv preprint arXiv:2204.07366},
year={2022}
```
## Third-party Implementation
[2022/05/26] ResT and ResT v2 have been integrated into [PaddleViT](https://github.com/BR-IDL/PaddleViT), checkout [here](https://github.com/BR-IDL/PaddleViT/tree/develop/image_classification/ResT) for the 3rd party implementation on Paddle framework!