Overview
Swin Transformer is a hierarchical vision transformer designed as a general-purpose backbone for computer vision tasks. It employs a shifted windowing scheme to compute representations, limiting self-attention to non-overlapping local windows while enabling cross-window connections. This architecture offers greater efficiency and achieves strong performance in tasks like image classification, object detection, and semantic segmentation. The implementation supports various follow-up works including Video Swin Transformer for video action recognition, and SimMIM for masked image modeling based pre-training. It integrates with tools like FasterTransformer for optimized inference on Nvidia GPUs and Tutel for Mixture-of-Experts variants. The model allows feature distillation to improve fine-tuning performance across different pre-trained models.