hzhang57 / rest

This is an official implementation for "ResT: An Efficient Transformer for Visual Recognition".

ResT

By Qing-Long Zhang and Yu-Bin Yang

[State Key Laboratory for Novel Software Technology at Nanjing University]

This repo is the official implementation of "ResT: An Efficient Transformer for Visual Recognition". It currently includes code and models for the following tasks:

Image Classification: Included in this repo. See get_started.md for a quick start.

Object Detection and Instance Segmentation: Based on detectron2, coming soon.

ResT is initially described in arxiv, 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.

Main Results on ImageNet with Pretrained Models

ImageNet-1K Pretrained Models

name	resolution	acc@1	acc@5	#params	FLOPs	FPS	1K model
ResT-Lite	224x224	77.2	93.7	10.5M	1.4G	1246	baidu
ResT-Small	224x224	79.6	94.9	13.7M	1.9G	1043	baidu
ResT-Base	224x224	81.6	95.7	30.3M	4.3G	673	baidu
ResT-Large	224x224	83.6	96.3	51.6M	7.9G	429	baidu

Note: access code for baidu is rest.

Citing ResT

@article{zhql2021ResT,
  title={ResT: An Efficient Transformer for Visual Recognition},
  author={Zhang, Qinglong and Yang, Yubin},
  journal={arXiv preprint arXiv:2105.13677v2},
  year={2021}
}