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

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Deep LearningResT
Overview

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 [email protected] [email protected] #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}
}
Owner
zhql
Machine Learning Bricklayer
zhql
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