/Swin-Transformer

This is an official implementation for "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows".

Primary LanguagePythonMIT LicenseMIT

Swin Transformer

PWC PWC PWC PWC PWC PWC

By Ze Liu*, Yutong Lin*, Yue Cao*, Han Hu*, Yixuan Wei, Zheng Zhang, Stephen Lin and Baining Guo.

This repo is the official implementation of "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows". 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: See Swin Transformer for Object Detection.

Semantic Segmentation: See Swin Transformer for Semantic Segmentation.

Updates

04/12/2021

Initial commits:

  1. Pretrained models on ImageNet-1K (Swin-T-IN1K, Swin-S-IN1K, Swin-B-IN1K) and ImageNet-22K (Swin-B-IN22K, Swin-L-IN22K) are provided.
  2. The supported code and models for ImageNet-1K image classification, COCO object detection and ADE20K semantic segmentation are provided.
  3. The cuda kernel implementation for the local relation layer is provided in branch LR-Net.

Introduction

Swin Transformer is initially described in arxiv, which capably serves as a general-purpose backbone for computer vision. It is basically a hierarchical Transformer whose representation is computed with shifted windows. The shifted windowing scheme brings greater efficiency by limiting self-attention computation to non-overlapping local windows while also allowing for cross-window connection.

Swin Transformer achieves strong performance on COCO object detection (58.7 box AP and 51.1 mask AP on test-dev) and ADE20K semantic segmentatiion (53.5 mIoU on val), surpassing previous models by a large margin.

teaser

Main Results on ImageNet with Pretrained Models

ImageNet-1K and ImageNet-22K Pretrained Models

name pretrain resolution acc@1 acc@5 #params FLOPs FPS 22K model 1K model
Swin-T ImageNet-1K 224x224 81.2 95.5 28M 4.5G 755 - github/baidu
Swin-S ImageNet-1K 224x224 83.2 96.2 50M 8.7G 437 - github/baidu
Swin-B ImageNet-1K 224x224 83.5 96.5 88M 15.4G 278 - github/baidu
Swin-B ImageNet-1K 384x384 84.5 97.0 88M 47.1G 85 - github/baidu
Swin-B ImageNet-22K 224x224 85.2 97.5 88M 15.4G 278 github/baidu github/baidu
Swin-B ImageNet-22K 384x384 86.4 98.0 88M 47.1G 85 github/baidu github/baidu
Swin-L ImageNet-22K 224x224 86.3 97.9 197M 34.5G 141 github/baidu github/baidu
Swin-L ImageNet-22K 384x384 87.3 98.2 197M 103.9G 42 github/baidu github/baidu

Note: access code for baidu is swin.

Main Results on Downstream Tasks

COCO Object Detection (2017 val)

Backbone Method pretrain Lr Schd box mAP mask mAP #params FLOPs
Swin-T Mask R-CNN ImageNet-1K 3x 46.0 41.6 48M 267G
Swin-S Mask R-CNN ImageNet-1K 3x 48.5 43.3 69M 359G
Swin-T Cascade Mask R-CNN ImageNet-1K 3x 50.4 43.7 86M 745G
Swin-S Cascade Mask R-CNN ImageNet-1K 3x 51.9 45.0 107M 838G
Swin-B Cascade Mask R-CNN ImageNet-1K 3x 51.9 45.0 145M 982G
Swin-T RepPoints V2 ImageNet-1K 3x 50.0 - 45M 283G
Swin-T Mask RepPoints V2 ImageNet-1K 3x 50.3 43.6 47M 292G
Swin-B HTC++ ImageNet-22K 6x 56.4 49.1 160M 1043G
Swin-L HTC++ ImageNet-22K 3x 57.1 49.5 284M 1470G
Swin-L HTC++* ImageNet-22K 3x 58.0 50.4 284M -

Note: * indicates multi-scale testing.

ADE20K Semantic Segmentation (val)

Backbone Method pretrain Crop Size Lr Schd mIoU mIoU (ms+flip) #params FLOPs
Swin-T UPerNet ImageNet-1K 512x512 160K 44.51 45.81 60M 945G
Swin-S UperNet ImageNet-1K 512x512 160K 47.64 49.47 81M 1038G
Swin-B UperNet ImageNet-1K 512x512 160K 48.13 49.72 121M 1188G
Swin-B UPerNet ImageNet-22K 640x640 160K 50.04 51.66 121M 1841G
Swin-L UperNet ImageNet-22K 640x640 160K 52.05 53.53 234M 3230G

Citing Swin Transformer

@article{liu2021Swin,
  title={Swin Transformer: Hierarchical Vision Transformer using Shifted Windows},
  author={Liu, Ze and Lin, Yutong and Cao, Yue and Hu, Han and Wei, Yixuan and Zhang, Zheng and Lin, Stephen and Guo, Baining},
  journal={arXiv preprint arXiv:2103.14030},
  year={2021}
}

Getting Started

Contributing

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.

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This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.

Trademarks

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