  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 \textbf{S}hifted \textbf{win}dows. 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. The code and models are publicly available
at~\url{https://github.com/microsoft/Swin-Transformer}.
