  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 (86.4 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 code and models will be made
publicly available at~\url{https://github.com/microsoft/Swin-Transformer}.
