HuCaoFighting/Swin-Unet

Health v2 · maintenance only

Flags: no_releases no_license

Adoption not part of the score

2416 stars · 367 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Official PyTorch implementation of Swin-Unet, a U-shaped pure Transformer model for medical image segmentation, published at ECCV 2022 Medical Computer Vision Workshop. It includes training and testing scripts for the Synapse and ACDC datasets using pretrained Swin Transformer weights.

Use cases

• segment medical images with a pure transformer model
• reproduce Swin-Unet paper results on Synapse and ACDC
• train a UNet-style transformer for organ segmentation
• compare transformer-based segmentation against CNN baselines
• fine-tune Swin Transformer for 2D medical segmentation
• use pretrained Swin weights for downstream segmentation tasks

When to choose

• you need a pure transformer architecture for 2D medical image segmentation
• you want to reproduce or build on the ECCV 2022 Swin-Unet paper
• you have GPU resources and pretrained Swin-T weights available
• you are researching transformer-based segmentation on Synapse or ACDC datasets

When to avoid

• you need a production-ready, licensed, maintained library (no license is provided)
• you need 3D volumetric segmentation out of the box
• you lack GPU memory or pretrained weights, which the model depends on
• you need guaranteed reproducibility across different GPU types

Facets

library · maturity stable

machine-learning deep-learning image-processing computer-vision deep-learning computer-vision healthcare python segmentation transformer swin-transformer unet medical-image-segmentation research-code eccv-2022 medical-imaging gpu linux

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