https://github.com/Beckschen/TransMix

[CVPR 2022] This repository includes the official project for the paper: TransMix: Attend to Mix for Vision Transformers.
https://github.com/Beckschen/TransMix

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[CVPR 2022] This repository includes the official project for the paper: TransMix: Attend to Mix for Vision Transformers.

• Host: GitHub
• URL: https://github.com/Beckschen/TransMix
• Owner: Beckschen
• License: apache-2.0
• Created: 2021-11-17T08:05:58.000Z (almost 3 years ago)
• Default Branch: main
• Last Pushed: 2022-12-06T16:28:15.000Z (almost 2 years ago)
• Last Synced: 2024-05-18T14:34:20.271Z (6 months ago)
• Language: Python
• Homepage:
• Size: 513 KB
• Stars: 156
• Watchers: 11
• Forks: 12
• Open Issues: 8
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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# TransMix: Attend to Mix for Vision Transformers

This repository includes the official project for the paper: [*TransMix: Attend to Mix for Vision Transformers*](https://arxiv.org/abs/2111.09833), CVPR 2022



  





  



# Key Feature

Improve your Vision Transformer (ViT) by ~1% on ImageNet with minimal computational cost and a simple ```--transmix```.

# Getting Started

First, clone the repo:

```shell

git clone https://github.com/Beckschen/TransMix.git

```

Then, you need to install the required packages including: [Pytorch](https://pytorch.org/) version 1.7.1,

[torchvision](https://pytorch.org/vision/stable/index.html) version 0.8.2,

[Timm](https://github.com/rwightman/pytorch-image-models) version 0.5.4

and ```pyyaml```. To install all these packages, simply run

```

pip3 install -r requirements.txt

```

Download and extract the [ImageNet](https://imagenet.stanford.edu/) dataset to ```data``` folder. Suppose you're using

8 GPUs for training, then simply run 

```shell

bash ./distributed_train.sh 8 data/ --config $YOUR_CONFIG_PATH_HERE

```

By default, all our config files have enabled the training with TransMix.

If you want to enable TransMix during the training of your own model,

you can add a ```--transmix``` in your training script. For example:

```shell

python3 -m torch.distributed.launch --nproc_per_node=8 train.py data/ --config $YOUR_CONFIG_PATH_HERE --transmix

```

Or you can simply specify ```transmix: True``` in your ```yaml``` config file like what we did in [deit_s_transmix](configs/deit_s_transmix.yaml).

To evaluate your model trained with TransMix, please refer to [timm](https://github.com/rwightman/pytorch-image-models#train-validation-inference-scripts).

You can also find your validation accuracy during training.

# Model Zoo

Coming soon!

## Acknowledgement 

This repository is built using the [Timm](https://github.com/rwightman/pytorch-image-models) library and 

the [DeiT](https://github.com/facebookresearch/deit) repository.

## License

This repository is released under the Apache 2.0 license as found in the [LICENSE](LICENSE) file.

## Cite This Paper

If you find our code helpful for your research, please using the following bibtex to cite our paper:

```

@InProceedings{transmix,

title = {TransMix: Attend to Mix for Vision Transformers},

author = {Chen, Jie-Neng and Sun, Shuyang and He, Ju and Torr, Philip and Yuille, Alan and Bai, Song},

booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},

month = {June},

year = {2022}

}

```