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Swin-Transformer

简介

Swin Transformer (the name Swin stands for Shifted window) is initially described in the paper, 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.

摘要

Show the paper's abstract
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 **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. 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.

使用方式

from mmpretrain import inference_model

predict = inference_model('swin-tiny_16xb64_in1k', 'demo/bird.JPEG')
print(predict['pred_class'])
print(predict['pred_score'])

Models and results

Image Classification on ImageNet-1k

模型

预训练

Params (M)

Flops (G)

Top-1 (%)

Top-5 (%)

配置文件

下载

swin-tiny_16xb64_in1k

从头训练

28.29

4.36

81.18

95.61

config

model | log

swin-small_16xb64_in1k

从头训练

49.61

8.52

83.02

96.29

config

model | log

swin-base_16xb64_in1k

从头训练

87.77

15.14

83.36

96.44

config

model | log

swin-tiny_3rdparty_in1k*

从头训练

28.29

4.36

81.18

95.52

config

model

swin-small_3rdparty_in1k*

从头训练

49.61

8.52

83.21

96.25

config

model

swin-base_3rdparty_in1k*

从头训练

87.77

15.14

83.42

96.44

config

model

swin-base_3rdparty_in1k-384*

从头训练

87.90

44.49

84.49

96.95

config

model

swin-base_in21k-pre-3rdparty_in1k*

从头训练

87.77

15.14

85.16

97.50

config

model

swin-base_in21k-pre-3rdparty_in1k-384*

从头训练

87.90

44.49

86.44

98.05

config

model

swin-large_in21k-pre-3rdparty_in1k*

从头训练

196.53

34.04

86.24

97.88

config

model

swin-large_in21k-pre-3rdparty_in1k-384*

从头训练

196.74

100.04

87.25

98.25

config

model

Models with * are converted from the official repo. The config files of these models are only for inference. We haven’t reproduce the training results.

Image Classification on CUB-200-2011

模型

预训练

Params (M)

Flops (G)

Top-1 (%)

配置文件

下载

swin-large_8xb8_cub-384px

从头训练

195.51

100.04

91.87

config

model | log

引用

@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}
}
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