swin-transformer-pytorch

Why we included this project

Anyone who wants to dissect the Swin Transformer's shifted-window attention will appreciate this early PyTorch implementation, which came out before Microsoft's official release. Self-attention is confined to local windows, and those windows shift between blocks, so the model stays efficient on high-resolution images while still letting information flow across window boundaries as the hierarchy deepens. The repository provides a clean network class rather than a full training pipeline; you set stage depths, head counts, window sizes, and downscaling factors, then drop it into classification, detection, or segmentation experiments. It installs via pip, carries an MIT license, and remains close enough to the paper to be a reliable reference for verifying the architecture or prototyping custom variants.