Swin Transformer

This repo is the official implementation of "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows" as well as the follow-ups. 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.

Video Action Recognition: See Video Swin Transformer.

Semi-Supervised Object Detection: See Soft Teacher.

SSL: Contrasitive Learning: See Transformer-SSL.

SSL: Masked Image Modeling: See get_started.md#simmim-support.

Mixture-of-Experts: See get_started for more instructions.

Feature-Distillation: See Feature-Distillation.

Updates

11/30/2022

Models and codes of Feature Distillation are released. Please refer to Feature-Distillation for details, and the checkpoints (FD-EsViT-Swin-B, FD-DeiT-ViT-B, FD-DINO-ViT-B, FD-CLIP-ViT-B, FD-CLIP-ViT-L).

09/24/2022

Merged SimMIM, which is a Masked Image Modeling based pre-training approach applicable to Swin and SwinV2 (and also applicable for ViT and ResNet). Please refer to get started with SimMIM to play with SimMIM pre-training.
Released a series of Swin and SwinV2 models pre-trained using the SimMIM approach (see MODELHUB for SimMIM), with model size ranging from SwinV2-Small-50M to SwinV2-giant-1B, data size ranging from ImageNet-1K-10% to ImageNet-22K, and iterations from 125k to 500k. You may leverage these models to study the properties of MIM methods. Please look into the data scaling paper for more details.

07/09/2022

News:

SwinV2-G achieves 61.4 mIoU on ADE20K semantic segmentation (+1.5 mIoU over the previous SwinV2-G model), using an additional feature distillation (FD) approach, setting a new recrod on this benchmark. FD is an approach that can generally improve the fine-tuning performance of various pre-trained models, including DeiT, DINO, and CLIP. Particularly, it improves CLIP pre-trained ViT-L by +1.6% to reach 89.0% on ImageNet-1K image classification, which is the most accurate ViT-L model.
Merged a PR from Nvidia that links to faster Swin Transformer inference that have significant speed improvements on T4 and A100 GPUs.
Merged a PR from Nvidia that enables an option to use pure FP16 (Apex O2) in training, while almost maintaining the accuracy.

06/03/2022

Added Swin-MoE, the Mixture-of-Experts variant of Swin Transformer implemented using Tutel (an optimized Mixture-of-Experts implementation). Swin-MoE is introduced in the TuTel paper.

05/12/2022

Pretrained models of Swin Transformer V2 on ImageNet-1K and ImageNet-22K are released.
ImageNet-22K pretrained models for Swin-V1-Tiny and Swin-V2-Small are released.

03/02/2022

Swin Transformer V2 and SimMIM got accepted by CVPR 2022. SimMIM is a self-supervised pre-training approach based on masked image modeling, a key technique that works out the 3-billion-parameter Swin V2 model using 40x less labelled data than that of previous billion-scale models based on JFT-3B.

02/09/2022

Integrated into Huggingface Spaces 🤗 using Gradio. Try out the Web Demo

10/12/2021

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

08/09/2021

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

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

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

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:

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.
The supported code and models for ImageNet-1K image classification, COCO object detection and ADE20K semantic segmentation are provided.
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.

Main Results on ImageNet with Pretrained Models

ImageNet-1K and ImageNet-22K Pretrained Swin-V1 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/log
Swin-S	ImageNet-1K	224x224	83.2	96.2	50M	8.7G	437	-	github/baidu/config/log
Swin-B	ImageNet-1K	224x224	83.5	96.5	88M	15.4G	278	-	github/baidu/config/log
Swin-B	ImageNet-1K	384x384	84.5	97.0	88M	47.1G	85	-	github/baidu/config
Swin-T	ImageNet-22K	224x224	80.9	96.0	28M	4.5G	755	github/baidu/config	github/baidu/config
Swin-S	ImageNet-22K	224x224	83.2	97.0	50M	8.7G	437	github/baidu/config	github/baidu/config
Swin-B	ImageNet-22K	224x224	85.2	97.5	88M	15.4G	278	github/baidu/config	github/baidu/config
Swin-B	ImageNet-22K	384x384	86.4	98.0	88M	47.1G	85	github/baidu	github/baidu/config
Swin-L	ImageNet-22K	224x224	86.3	97.9	197M	34.5G	141	github/baidu/config	github/baidu/config
Swin-L	ImageNet-22K	384x384	87.3	98.2	197M	103.9G	42	github/baidu	github/baidu/config

ImageNet-1K and ImageNet-22K Pretrained Swin-V2 Models

name	pretrain	resolution	window	acc@1	acc@5	#params	FLOPs	FPS	22K model	1K model
SwinV2-T	ImageNet-1K	256x256	8x8	81.8	95.9	28M	5.9G	572	-	github/baidu/config
SwinV2-S	ImageNet-1K	256x256	8x8	83.7	96.6	50M	11.5G	327	-	github/baidu/config
SwinV2-B	ImageNet-1K	256x256	8x8	84.2	96.9	88M	20.3G	217	-	github/baidu/config
SwinV2-T	ImageNet-1K	256x256	16x16	82.8	96.2	28M	6.6G	437	-	github/baidu/config
SwinV2-S	ImageNet-1K	256x256	16x16	84.1	96.8	50M	12.6G	257	-	github/baidu/config
SwinV2-B	ImageNet-1K	256x256	16x16	84.6	97.0	88M	21.8G	174	-	github/baidu/config
SwinV2-B^*	ImageNet-22K	256x256	16x16	86.2	97.9	88M	21.8G	174	github/baidu/config	github/baidu/config
SwinV2-B^*	ImageNet-22K	384x384	24x24	87.1	98.2	88M	54.7G	57	github/baidu/config	github/baidu/config
SwinV2-L^*	ImageNet-22K	256x256	16x16	86.9	98.0	197M	47.5G	95	github/baidu/config	github/baidu/config
SwinV2-L^*	ImageNet-22K	384x384	24x24	87.6	98.3	197M	115.4G	33	github/baidu/config	github/baidu/config

Note:

SwinV2-B^* (SwinV2-L^*) with input resolution of 256x256 and 384x384 both fine-tuned from the same pre-training model using a smaller input resolution of 192x192.
SwinV2-B^* (384x384) achieves 78.08 acc@1 on ImageNet-1K-V2 while SwinV2-L^* (384x384) achieves 78.31.

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.

ImageNet-22K Pretrained Swin-MoE Models

Please refer to get_started for instructions on running Swin-MoE.
Pretrained models for Swin-MoE can be found in MODEL HUB

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

@inproceedings{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},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
  year={2021}
}

Citing Local Relation Networks (the first full-attention visual backbone)

@inproceedings{hu2019local,
  title={Local Relation Networks for Image Recognition},
  author={Hu, Han and Zhang, Zheng and Xie, Zhenda and Lin, Stephen},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
  pages={3464--3473},
  year={2019}
}

Citing Swin Transformer V2

@inproceedings{liu2021swinv2,
  title={Swin Transformer V2: Scaling Up Capacity and Resolution}, 
  author={Ze Liu and Han Hu and Yutong Lin and Zhuliang Yao and Zhenda Xie and Yixuan Wei and Jia Ning and Yue Cao and Zheng Zhang and Li Dong and Furu Wei and Baining Guo},
  booktitle={International Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2022}
}

Citing SimMIM (a self-supervised approach that enables SwinV2-G)

@inproceedings{xie2021simmim,
  title={SimMIM: A Simple Framework for Masked Image Modeling},
  author={Xie, Zhenda and Zhang, Zheng and Cao, Yue and Lin, Yutong and Bao, Jianmin and Yao, Zhuliang and Dai, Qi and Hu, Han},
  booktitle={International Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2022}
}

Citing SimMIM-data-scaling

@article{xie2022data,
  title={On Data Scaling in Masked Image Modeling},
  author={Xie, Zhenda and Zhang, Zheng and Cao, Yue and Lin, Yutong and Wei, Yixuan and Dai, Qi and Hu, Han},
  journal={arXiv preprint arXiv:2206.04664},
  year={2022}
}

Citing Swin-MoE

@misc{hwang2022tutel,
      title={Tutel: Adaptive Mixture-of-Experts at Scale}, 
      author={Changho Hwang and Wei Cui and Yifan Xiong and Ziyue Yang and Ze Liu and Han Hu and Zilong Wang and Rafael Salas and Jithin Jose and Prabhat Ram and Joe Chau and Peng Cheng and Fan Yang and Mao Yang and Yongqiang Xiong},
      year={2022},
      eprint={2206.03382},
      archivePrefix={arXiv}
}

Getting Started

For Image Classification, please see get_started.md for detailed instructions.
For Object Detection and Instance Segmentation, please see Swin Transformer for Object Detection.
For Semantic Segmentation, please see Swin Transformer for Semantic Segmentation.
For Self-Supervised Learning, please see Transformer-SSL.
For Video Recognition, please see Video Swin Transformer.

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)

[06/30/2022] Swin Transformers (V1) inference implemented in FasterTransformer: FasterTransformer

[05/12/2022] Swin Transformers (V1) implemented in TensorFlow with the pre-trained parameters ported into them. Find the implementation, TensorFlow weights, code example here in this repository.

[04/06/2022] Swin Transformer for Audio Classification: Hierarchical Token Semantic Audio Transformer.

[12/21/2021] Swin Transformer for StyleGAN: StyleSwin

[12/13/2021] Swin Transformer for Face Recognition: FaceX-Zoo

[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.

When you submit a pull request, a CLA bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.

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

This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow Microsoft's Trademark & Brand Guidelines. Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party's policies.

ts0923/Swin-Transformer