This repo is the official implementation of "CSWin Transformer: A General Vision Transformer Backbone with Cross-Shaped Windows". The code and models for downstream tasks are coming soon.

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 [email protected] #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 50.4 60M 959G
CSwin-S UperNet ImageNet-1K 512x512 160K 50.0 50.8 65M 1027G
CSwin-B UperNet ImageNet-1K 512x512 160K 50.8 51.7 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


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

bash install_req.sh

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

git clone https://github.com/NVIDIA/apex
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 train.sh 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 train.sh 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 train.sh 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 finetune.sh 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 finetune.sh 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.