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.
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.
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.
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 |
Swin-S | ImageNet-1K | 224x224 | 83.2 | 96.2 | 50M | 8.7G | 437 | - | github/baidu |
Swin-B | ImageNet-1K | 224x224 | 83.5 | 96.5 | 88M | 15.4G | 278 | - | github/baidu |
Swin-B | ImageNet-1K | 384x384 | 84.5 | 97.0 | 88M | 47.1G | 85 | - | github/baidu |
Swin-B | ImageNet-22K | 224x224 | 85.2 | 97.5 | 88M | 15.4G | 278 | github/baidu | github/baidu |
Swin-B | ImageNet-22K | 384x384 | 86.4 | 98.0 | 88M | 47.1G | 85 | github/baidu | github/baidu |
Swin-L | ImageNet-22K | 224x224 | 86.3 | 97.9 | 197M | 34.5G | 141 | github/baidu | github/baidu |
Swin-L | ImageNet-22K | 384x384 | 87.3 | 98.2 | 197M | 103.9G | 42 | github/baidu | github/baidu |
Note: access code for baidu
is swin
.
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 |
@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={arXiv preprint arXiv:2103.14030},
year={2021}
}
- 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.
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
)
[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.
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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