# Image Classification Backbone

FocalNet is an image classification model based on the focal modulation network, pretrained by the Microsoft team on the ImageNet-22k dataset.

Swin Transformer v2 is a vision transformer model that achieves efficient image classification and dense recognition tasks through hierarchical feature maps and local window self-attention mechanisms.

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

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

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 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 based on shifted windows, 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.

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

Swin Transformer is a hierarchical vision transformer based on shifted windows, specifically designed for image classification tasks, with computational complexity linear to input image size.