/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 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.

Self-Supervised Learning: See Transformer-SSL.

Video Action Recognition: See Video Swin Transformer.

Semi-Supervised Object Detection: See Soft Teacher.

Updates

10/12/2021

News: Swin Transformer received ICCV 2021 best paper award (Marr Prize).

08/09/2021

  1. Soft Teacher will appear at ICCV2021. The code will be released at GitHub Repo. Soft Teacher is an end-to-end semi-supervisd object detection method, achieving a new record on the COCO test-dev: 61.3 box AP and 53.0 mask AP.

07/03/2021

  1. Add Swin MLP, which is an adaption of Swin Transformer by replacing all multi-head self-attention (MHSA) blocks by MLP layers (more precisely it is a group linear layer). The shifted window configuration can also significantly improve the performance of vanilla MLP architectures.

06/25/2021

  1. Video Swin Transformer is released at Video-Swin-Transformer. Video Swin Transformer achieves state-of-the-art accuracy on a broad range of video recognition benchmarks, including action recognition (84.9 top-1 accuracy on Kinetics-400 and 86.1 top-1 accuracy on Kinetics-600 with ~20x less pre-training data and ~3x smaller model size) and temporal modeling (69.6 top-1 accuracy on Something-Something v2).

05/12/2021

  1. Used as a backbone for Self-Supervised Learning: Transformer-SSL

Using Swin-Transformer as the backbone for self-supervised learning enables us to evaluate the transferring performance of the learnt representations on down-stream tasks, which is missing in previous works due to the use of ViT/DeiT, which has not been well tamed for down-stream tasks.

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 (the name Swin stands for Shifted window) 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 segmentation (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/config
Swin-S ImageNet-1K 224x224 83.2 96.2 50M 8.7G 437 - github/baidu/config
Swin-B ImageNet-1K 224x224 83.5 96.5 88M 15.4G 278 - github/baidu/config
Swin-B ImageNet-1K 384x384 84.5 97.0 88M 47.1G 85 - github/baidu/test-config
Swin-B ImageNet-22K 224x224 85.2 97.5 88M 15.4G 278 github/baidu github/baidu/test-config
Swin-B ImageNet-22K 384x384 86.4 98.0 88M 47.1G 85 github/baidu github/baidu/test-config
Swin-L ImageNet-22K 224x224 86.3 97.9 197M 34.5G 141 github/baidu github/baidu/test-config
Swin-L ImageNet-22K 384x384 87.3 98.2 197M 103.9G 42 github/baidu github/baidu/test-config

ImageNet-1K Pretrained Swin MLP Models

name pretrain resolution acc@1 acc@5 #params FLOPs FPS 1K model
Mixer-B/16 ImageNet-1K 224x224 76.4 - 59M 12.7G - official repo
ResMLP-S24 ImageNet-1K 224x224 79.4 - 30M 6.0G 715 timm
ResMLP-B24 ImageNet-1K 224x224 81.0 - 116M 23.0G 231 timm
Swin-T/C24 ImageNet-1K 256x256 81.6 95.7 28M 5.9G 563 github/baidu/config
SwinMLP-T/C24 ImageNet-1K 256x256 79.4 94.6 20M 4.0G 807 github/baidu/config
SwinMLP-T/C12 ImageNet-1K 256x256 79.6 94.7 21M 4.0G 792 github/baidu/config
SwinMLP-T/C6 ImageNet-1K 256x256 79.7 94.9 23M 4.0G 766 github/baidu/config
SwinMLP-B ImageNet-1K 224x224 81.3 95.3 61M 10.4G 409 github/baidu/config

Note: access code for baidu is swin. C24 means each head has 24 channels.

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={International Conference on Computer Vision (ICCV)},
  year={2021}
}

Getting Started

Third-party Usage and Experiments

In this pargraph, we cross link third-party repositories which use Swin and report results. You can let us know by raising an issue

(Note please report accuracy numbers and provide trained models in your new repository to facilitate others to get sense of correctness and model behavior)

[08/29/2021] Swin Transformer for Image Restoration: SwinIR

[08/12/2021] Swin Transformer for person reID: https://github.com/layumi/Person_reID_baseline_pytorch

[06/29/2021] Swin-Transformer in PaddleClas and inference based on whl package: https://github.com/PaddlePaddle/PaddleClas

[04/14/2021] Swin for RetinaNet in Detectron: https://github.com/xiaohu2015/SwinT_detectron2.

[04/16/2021] Included in a famous model zoo: https://github.com/rwightman/pytorch-image-models.

[04/20/2021] Swin-Transformer classifier inference using TorchServe: https://github.com/kamalkraj/Swin-Transformer-Serve

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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