Papers › Swin Transformer: Hierarchical Vision Transformer using Shifted Windows

Swin Transformer: Hierarchical Vision Transformer using Shifted Windows

25 Mar 2021ICCV 2021 10arXiv:2103.14030archive 2025-07-28

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

This paper presents a new vision Transformer, called Swin Transformer, that capably serves as a general-purpose backbone for computer vision. Challenges in adapting Transformer from language to vision arise from differences between the two domains, such as large variations in the scale of visual entities and the high resolution of pixels in images compared to words in text. To address these differences, we propose a hierarchical Transformer whose representation is computed with \textbf{S}hifted \textbf{win}dows. The shifted windowing scheme brings greater efficiency by limiting self-attention computation to non-overlapping local windows while also allowing for cross-window connection. This hierarchical architecture has the flexibility to model at various scales and has linear computational complexity with respect to image size. These qualities of Swin Transformer make it compatible with a broad range of vision tasks, including image classification (87.3 top-1 accuracy on ImageNet-1K) and dense prediction tasks such as object detection (58.7 box AP and 51.1 mask AP on COCO test-dev) and semantic segmentation (53.5 mIoU on ADE20K val). Its performance surpasses the previous state-of-the-art by a large margin of +2.7 box AP and +2.6 mask AP on COCO, and +3.2 mIoU on ADE20K, demonstrating the potential of Transformer-based models as vision backbones. The hierarchical design and the shifted window approach also prove beneficial for all-MLP architectures. The code and models are publicly available at~\url{https://github.com/microsoft/Swin-Transformer}.

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microsoft/Swin-Transformer officialmentioned in papermentioned on GitHubpytorch report
rwightman/pytorch-image-models mentioned in papermentioned on GitHubpytorch report
AntixK/PyTorch-Model-Compare mentioned on GitHubpytorchMIT report
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LiWentomng/OrientedRepPoints mentioned on GitHubpytorch report
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NEUdeep/Swin-Transformer-Object-Detection mentioned on GitHubpytorchApache-2.0 report
SforAiDl/vformer mentioned on GitHubpytorch report
SwinTransformer/Transformer-SSL mentioned on GitHubpytorch report
USTC-IMCL/HST-for-Compressed-SR mentioned on GitHubpytorch report
WangFeng18/Swin-Transformer mentioned on GitHubpytorch report
YongWookHa/swin-transformer-ocr mentioned on GitHubpytorchMIT report
abman23/pmnet mentioned on GitHubpytorchMIT report
ayanglab/swinganmr mentioned on GitHubpytorch report
ayanglab/swinmr mentioned on GitHubpytorch report
befallenStar/molecularAtt mentioned on GitHubpytorch report
berniwal/swin-transformer-pytorch mentioned on GitHubpytorch report
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holdfire/FAS mentioned on GitHubpytorch report
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innat/VideoSwin mentioned on GitHubtf report
koechslin/swin-transformer-semantic-segmentation mentioned on GitHubpytorchApache-2.0 report
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30ran · fixture could not drive it
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Tasks

Image ClassificationInstance SegmentationObject DetectionReal-Time Object DetectionSemantic SegmentationThermal Image Segmentationimage-classification

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Image Classification ImageNet Swin-L GFLOPs 103.9 #96 of 1060 Archive leaderboard report
Image Classification ImageNet Swin-L Number of params 197M #96 of 1060 Archive leaderboard report
Image Classification ImageNet Swin-L Top 1 Accuracy 87.3% #96 of 1060 Archive leaderboard report
Image Classification ImageNet Swin-B GFLOPs 47 #149 of 1060 Archive leaderboard report
Image Classification ImageNet Swin-B Number of params 88M #149 of 1060 Archive leaderboard report
Image Classification ImageNet Swin-B Top 1 Accuracy 86.4% #149 of 1060 Archive leaderboard report
Image Classification ImageNet Swin-T GFLOPs 4.5 #651 of 1060 Archive leaderboard report
Image Classification ImageNet Swin-T Number of params 29M #651 of 1060 Archive leaderboard report
Image Classification ImageNet Swin-T Top 1 Accuracy 81.3% #651 of 1060 Archive leaderboard report
Image Classification OmniBenchmark SwinTransformer Average Top-1 Accuracy 46.4 #2 of 22 Archive leaderboard report
Instance Segmentation COCO minival Swin-L (HTC++, multi scale) mask AP 50.4 #24 of 93 Archive leaderboard report
Instance Segmentation COCO minival Swin-L (HTC++, single scale) mask AP 49.5 #27 of 93 Archive leaderboard report
Instance Segmentation COCO test-dev Swin-L (HTC++, multi scale) mask AP 51.1 #20 of 112 Archive leaderboard report
Instance Segmentation COCO test-dev Swin-L (HTC++, single scale) mask AP 50.2 #22 of 112 Archive leaderboard report
Instance Segmentation Occluded COCO Swin-B + Cascade Mask R-CNN Mean Recall 62.90 #2 of 6 Archive leaderboard report
Instance Segmentation Occluded COCO Swin-S + Mask R-CNN Mean Recall 61.14 #5 of 6 Archive leaderboard report
Instance Segmentation Occluded COCO Swin-T + Mask R-CNN Mean Recall 58.81 #6 of 6 Archive leaderboard report
Instance Segmentation Separated COCO Swin-B + Cascade Mask R-CNN Mean Recall 36.31 #2 of 6 Archive leaderboard report
Instance Segmentation Separated COCO Swin-S + Mask R-CNN Mean Recall 33.67 #5 of 6 Archive leaderboard report
Instance Segmentation Separated COCO Swin-T + Mask R-CNN Mean Recall 31.94 #6 of 6 Archive leaderboard report
Object Detection COCO minival Swin-L (HTC++, multi scale) box AP 58 #37 of 220 Archive leaderboard report
Object Detection COCO minival Swin-L (HTC++, single scale) box AP 57.1 #41 of 220 Archive leaderboard report
Object Detection COCO test-dev Swin-L (HTC++, multi scale) box mAP 58.7 #35 of 225 Archive leaderboard report
Object Detection COCO test-dev Swin-L (HTC++, single scale) box mAP 57.7 #36 of 225 Archive leaderboard report
Semantic Segmentation ADE20K Swin-L (UperNet, ImageNet-22k pretrain) Test Score 62.8 #80 of 235 Archive leaderboard report
Semantic Segmentation ADE20K Swin-L (UperNet, ImageNet-22k pretrain) Validation mIoU 53.50 #80 of 235 Archive leaderboard report
Semantic Segmentation ADE20K Swin-B (UperNet, ImageNet-1k pretrain) Validation mIoU 49.7 #127 of 235 Archive leaderboard report
Semantic Segmentation ADE20K val Swin-L (UperNet, ImageNet-22k pretrain) mIoU 53.5 #39 of 95 Archive leaderboard report
Semantic Segmentation ADE20K val Swin-B (UperNet, ImageNet-1k pretrain) mIoU 49.7 #54 of 95 Archive leaderboard report
Semantic Segmentation FoodSeg103 Swin-Transformer (Swin-Small) mIoU 41.6 #4 of 7 Archive leaderboard report
Thermal Image Segmentation MFN Dataset SwinT mIOU 49.0 #42 of 55 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Absolute Position EncodingsAdamWAttentionBPECosine AnnealingDense ConnectionsDropoutLabel SmoothingLayer NormalizationLinear LayerMulti-Head AttentionPosition-Wise Feed-Forward LayerResidual ConnectionSoftmaxStochastic DepthSwin TransformerTransformer

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections