LeeJunHyun/Image_Segmentation

Health v2 · maintenance only

Flags: no_releases no_license

Adoption not part of the score

3101 stars · 632 forks observed · 2026-08-28

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

A PyTorch implementation of four U-Net variants for image segmentation: U-Net, R2U-Net, Attention U-Net, and Attention R2U-Net. It includes model architectures and evaluation on the ISIC 2018 skin lesion dataset.

Use cases

• segment medical images like skin lesions
• train a U-Net in PyTorch
• compare attention-based U-Net variants
• reproduce R2U-Net paper results
• learn how attention U-Net is implemented

When to choose

• you need reference PyTorch code for U-Net variants
• you are reproducing the R2U-Net or Attention U-Net papers
• you want a simple starting point for biomedical segmentation experiments

When to avoid

• you need a maintained, production-ready segmentation library
• you want broad dataset support beyond ISIC-style data
• you need a license permitting redistribution

Facets

library · maturity abandoned

machine-learning deep-learning image-processing computer-vision deep-learning computer-vision image-processing healthcare python pytorch unet image-segmentation attention medical-imaging research-code

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