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Swin Base Patch4 Window12 384 In22k

Developed by microsoft
Swin Transformer is a hierarchical vision Transformer based on shifted windows, specifically designed for image classification tasks.
Downloads 2,431
Release Time : 3/2/2022

Model Overview

This model is pretrained on the ImageNet-21k dataset and employs hierarchical feature maps and local window self-attention mechanisms, significantly reducing computational complexity.

Model Features

Hierarchical Feature Maps
Constructs hierarchical feature maps by merging deep image patches, suitable for processing visual information at different scales.
Local Window Self-Attention
Computes self-attention only within local windows, resulting in computational complexity that scales linearly with input image size.
Efficient Architecture
Significantly reduces computational complexity compared to traditional vision Transformers, making it suitable as a general-purpose backbone network.

Model Capabilities

Image Classification
Visual Feature Extraction

Use Cases

Computer Vision
General Image Classification
Classifies input images into one of the 21,841 categories in the ImageNet-21k dataset.
Dense Recognition Tasks
Can serve as a backbone network for dense recognition tasks such as object detection and semantic segmentation.
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