S

Swin Base Patch4 Window7 224

Developed by microsoft
Swin Transformer is a hierarchical vision transformer based on shifted windows, suitable for image classification tasks.
Downloads 281.49k
Release Time : 3/2/2022

Model Overview

This model was trained on the ImageNet-1k dataset at 224x224 resolution, employing hierarchical feature maps and local window self-attention mechanisms with computational complexity linear to input image size.

Model Features

Hierarchical Feature Maps
Constructs hierarchical feature maps by merging image patches, suitable for visual tasks at different scales.
Local Window Self-Attention
Computes self-attention only within local windows, resulting in computational complexity linear to input image size.
General Backbone Network
Can serve as a general backbone network for image classification and dense recognition tasks.

Model Capabilities

Image classification
Visual feature extraction

Use Cases

Computer Vision
Image classification
Classifies images into one of the 1,000 categories in ImageNet.
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