This repo is the official implementation of "Self-slimmed Vision Transformer".

07/20/2022

[Initial commits]:

1. The supported code and models for LV-ViT are provided.

SiT (Self-slimmed Vision Transformer) is introduce in arxiv and serves as a generic self-slimmed learning method for vanilla vision transformers. Our concise TSM (Token Slimming Module) softly integrates redundant tokens into fewer informative ones. For stable and efficient training, we introduce a novel FRD framework to leverage structure knowledge, which can densely transfer token information in a flexible auto-encoder manner.

Our SiT can speed up ViTs by 1.7x with negligible accuracy drop, and even speed up ViTs by 3.6x while maintaining 97% of their performance. Surprisingly, by simply arming LV-ViT with our SiT, we achieve new state-of-the-art performance on ImageNet, surpassing all the recent CNNs and ViTs.

We follow the settings of LeViT for inference speed evaluation.

The LV-ViT teacher models are trained with token-labeling and their checkpoints are provided.

If you find this repository useful, please use the following BibTeX entry for citation.

@misc{zong2021self,
      title={Self-slimmed Vision Transformer}, 
      author={Zhuofan Zong and Kunchang Li and Guanglu Song and Yali Wang and Yu Qiao and Biao Leng and Yu Liu},
      year={2021},
      eprint={2111.12624},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

This project is released under the MIT license. Please see the LICENSE file for more information.