This repository provides a PyTorch implementation of the Swin Transformer architecture, a vision transformer designed for computer vision tasks. The model uses a shifted windowing scheme for self-attention to achieve linear computational complexity with respect to image size. It serves as a general-purpose backbone for tasks including image classification, object detection, and semantic segmentation, achieving strong performance on benchmarks such as ImageNet-1K, COCO, and ADE20K.