# Local Window Attention

Swin Transformer v2 is a vision Transformer model that achieves efficient image processing through hierarchical feature maps and local window self-attention mechanisms.

Swin Transformer is a hierarchical vision Transformer model based on shifted windows, specifically designed for image classification tasks.

Swin Transformer is a hierarchical window-based vision Transformer model pretrained on the ImageNet-21k dataset, suitable for image classification tasks.

Swin Transformer is a hierarchical window-based vision Transformer model designed for image classification tasks, with computational complexity linearly related to input image size.

Swin Transformer is a hierarchical window-based vision Transformer model, pretrained on the ImageNet-21k dataset, suitable for image classification tasks.

Swin Transformer is a hierarchical vision Transformer that achieves linear computational complexity by computing self-attention within local windows, making it suitable for image classification tasks.

Swin Transformer is a hierarchical vision Transformer that achieves linear computational complexity by computing self-attention within local windows, making it suitable for image classification and dense recognition tasks.