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CSWin Transformer: A General Vision Transformer Backbone with Cross-Shaped, CVPR 2022


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CSWin-Transformer, CVPR 2022


This repo is the official implementation of "CSWin Transformer: A General Vision Transformer Backbone with Cross-Shaped Windows".


CSWin Transformer (the name CSWin stands for Cross-Shaped Window) is introduced in arxiv, which is a new general-purpose backbone for computer vision. It is a hierarchical Transformer and replaces the traditional full attention with our newly proposed cross-shaped window self-attention. The cross-shaped window self-attention mechanism computes self-attention in the horizontal and vertical stripes in parallel that from a cross-shaped window, with each stripe obtained by splitting the input feature into stripes of equal width. With CSWin, we could realize global attention with a limited computation cost.

CSWin Transformer achieves strong performance on ImageNet classification (87.5 on val with only 97G flops) and ADE20K semantic segmentation (55.7 mIoU on val), surpassing previous models by a large margin.


Main Results on ImageNet

model pretrain resolution acc@1 #params FLOPs 22K model 1K model
CSWin-T ImageNet-1K 224x224 82.8 23M 4.3G - model
CSWin-S ImageNet-1k 224x224 83.6 35M 6.9G - model
CSWin-B ImageNet-1k 224x224 84.2 78M 15.0G - model
CSWin-B ImageNet-1k 384x384 85.5 78M 47.0G - model
CSWin-L ImageNet-22k 224x224 86.5 173M 31.5G model model
CSWin-L ImageNet-22k 384x384 87.5 173M 96.8G - model

Main Results on Downstream Tasks

COCO Object Detection

backbone Method pretrain lr Schd box mAP mask mAP #params FLOPS
CSwin-T Mask R-CNN ImageNet-1K 3x 49.0 43.6 42M 279G
CSwin-S Mask R-CNN ImageNet-1K 3x 50.0 44.5 54M 342G
CSwin-B Mask R-CNN ImageNet-1K 3x 50.8 44.9 97M 526G
CSwin-T Cascade Mask R-CNN ImageNet-1K 3x 52.5 45.3 80M 757G
CSwin-S Cascade Mask R-CNN ImageNet-1K 3x 53.7 46.4 92M 820G
CSwin-B Cascade Mask R-CNN ImageNet-1K 3x 53.9 46.4 135M 1004G

ADE20K Semantic Segmentation (val)

Backbone Method pretrain Crop Size Lr Schd mIoU mIoU (ms+flip) #params FLOPs
CSwin-T Semantic FPN ImageNet-1K 512x512 80K 48.2 - 26M 202G
CSwin-S Semantic FPN ImageNet-1K 512x512 80K 49.2 - 39M 271G
CSwin-B Semantic FPN ImageNet-1K 512x512 80K 49.9 - 81M 464G
CSwin-T UPerNet ImageNet-1K 512x512 160K 49.3 50.7 60M 959G
CSwin-S UperNet ImageNet-1K 512x512 160K 50.4 51.5 65M 1027G
CSwin-B UperNet ImageNet-1K 512x512 160K 51.1 52.2 109M 1222G
CSwin-B UPerNet ImageNet-22K 640x640 160K 51.8 52.6 109M 1941G
CSwin-L UperNet ImageNet-22K 640x640 160K 53.4 55.7 208M 2745G

pretrained models and code could be found at segmentation


timm==0.3.4, pytorch>=1.4, opencv, ... , run:


Apex for mixed precision training is used for finetuning. To install apex, run:

git clone
cd apex
pip install -v --disable-pip-version-check --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./

Data prepare: ImageNet with the following folder structure, you can extract imagenet by this script.

│  ├── n01440764
│  │   ├── n01440764_10026.JPEG
│  │   ├── n01440764_10027.JPEG
│  │   ├── ......
│  ├── ......
│  ├── n01440764
│  │   ├── ILSVRC2012_val_00000293.JPEG
│  │   ├── ILSVRC2012_val_00002138.JPEG
│  │   ├── ......
│  ├── ......


Train the three lite variants: CSWin-Tiny, CSWin-Small and CSWin-Base:

bash 8 --data <data path> --model CSWin_64_12211_tiny_224 -b 256 --lr 2e-3 --weight-decay .05 --amp --img-size 224 --warmup-epochs 20 --model-ema-decay 0.99984 --drop-path 0.2
bash 8 --data <data path> --model CSWin_64_24322_small_224 -b 256 --lr 2e-3 --weight-decay .05 --amp --img-size 224 --warmup-epochs 20 --model-ema-decay 0.99984 --drop-path 0.4
bash 8 --data <data path> --model CSWin_96_24322_base_224 -b 128 --lr 1e-3 --weight-decay .1 --amp --img-size 224 --warmup-epochs 20 --model-ema-decay 0.99992 --drop-path 0.5

If you want to train our CSWin on images with 384x384 resolution, please use '--img-size 384'.

If the GPU memory is not enough, please use '-b 128 --lr 1e-3 --model-ema-decay 0.99992' or use checkpoint '--use-chk'.


Finetune CSWin-Base with 384x384 resolution:

bash 8 --data <data path> --model CSWin_96_24322_base_384 -b 32 --lr 5e-6 --min-lr 5e-7 --weight-decay 1e-8 --amp --img-size 384 --warmup-epochs 0 --model-ema-decay 0.9998 --finetune <pretrained 224 model> --epochs 20 --mixup 0.1 --cooldown-epochs 10 --drop-path 0.7 --ema-finetune --lr-scale 1 --cutmix 0.1

Finetune ImageNet-22K pretrained CSWin-Large with 224x224 resolution:

bash 8 --data <data path> --model CSWin_144_24322_large_224 -b 64 --lr 2.5e-4 --min-lr 5e-7 --weight-decay 1e-8 --amp --img-size 224 --warmup-epochs 0 --model-ema-decay 0.9996 --finetune <22k-pretrained model> --epochs 30 --mixup 0.01 --cooldown-epochs 10 --interpolation bicubic  --lr-scale 0.05 --drop-path 0.2 --cutmix 0.3 --use-chk --fine-22k --ema-finetune

If the GPU memory is not enough, please use checkpoint '--use-chk'.

Cite CSWin Transformer

      title={CSWin Transformer: A General Vision Transformer Backbone with Cross-Shaped Windows}, 
        author={Xiaoyi Dong and Jianmin Bao and Dongdong Chen and Weiming Zhang and Nenghai Yu and Lu Yuan and Dong Chen and Baining Guo},


This repository is built using the timm library and the DeiT repository.


This project is licensed under the license found in the LICENSE file in the root directory of this source tree.

Microsoft Open Source Code of Conduct

Contact Information

For help or issues using CSWin Transformer, please submit a GitHub issue.

For other communications related to CSWin Transformer, please contact Jianmin Bao (, Dong Chen (


CSWin Transformer: A General Vision Transformer Backbone with Cross-Shaped, CVPR 2022



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