# LeeJunHyun/Image_Segmentation

Pytorch implementation of U-Net, R2U-Net, Attention U-Net, and Attention R2U-Net.

Repository: https://github.com/LeeJunHyun/Image_Segmentation
Canonical: https://ross.abutalabs.com/products/image_segmentation
Language: Python
License Family: other
Last push: 2023-06-30T05:20:58+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2998, "days_push": 1160, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 3101, forks 632 (observed 2026-08-28T04:07:43.556815+00:00)

## What it is
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
- artifact type: library
- maturity: abandoned
- function: machine-learning, deep-learning, image-processing, computer-vision
- domain: deep-learning, computer-vision, image-processing, healthcare
- platform: python
- tags: pytorch, unet, image-segmentation, attention, medical-imaging, research-code

## Member repositories
- LeeJunHyun/Image_Segmentation (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:43.556815+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T07:26:59.451517+00:00, confidence not recorded.
  - readme: https://github.com/LeeJunHyun/Image_Segmentation (fetched 2026-08-28T04:07:43.556815+00:00, sha da27ae8fe5e4)
- Data as of 2026-08-30T08:39:29.467469+00:00.