swin_transformer

Swin Transformer is a recently proposed transformer-based architecture for computer vision tasks. It stands for "Scales-Windows-Interpolation", which are the key concepts used in the design of the architecture.

The Swin Transformer aims to address the limitations of the standard transformer architecture in handling large input images. It achieves this by introducing a hierarchical approach where the input image is divided into smaller patches, which are then processed by transformer layers in a hierarchical manner. This allows the Swin Transformer to scale to larger input image sizes without increasing the computational cost.

The Swin Transformer also makes use of a shifted window approach, where the receptive field of each transformer layer is shifted by a certain offset. This helps to improve the coverage of the input image by the transformer layers and reduces the number of overlapping patches.

Finally, the Swin Transformer uses a combination of local and global attention mechanisms, which enables it to effectively capture both local and global context information from the input image.

Overall, the Swin Transformer has shown promising results on a range of computer vision tasks such as image classification, object detection, and semantic segmentation, and is likely to be an important architecture for future computer vision research.