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Swin Base Patch4 Window7 224 In22k

Developed by microsoft
Swin Transformer is a hierarchical window-based vision Transformer model pretrained on the ImageNet-21k dataset, suitable for image classification tasks.
Downloads 13.30k
Release Time : 3/2/2022

Model Overview

This model constructs hierarchical feature maps by computing self-attention within local windows, with computational complexity linear to input image size, making it an ideal general-purpose backbone for image classification and dense recognition tasks.

Model Features

Hierarchical Feature Maps
Constructs hierarchical feature maps by merging image patches at deeper layers to enhance feature extraction capability
Local Window Attention
Computes self-attention only within local windows, resulting in linear computational complexity relative to input image size
Efficient Computation
More computationally efficient compared to traditional vision Transformers that compute global self-attention

Model Capabilities

Image Classification
Visual Feature Extraction

Use Cases

Computer Vision
General Image Classification
Classifies images into one of 21,841 categories in the ImageNet-21k dataset
Visual Feature Extraction
Serves as a backbone network to provide feature representations for other vision tasks
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