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.
