# MIC-DKFZ/nnUNet

Repository: https://github.com/MIC-DKFZ/nnUNet
Canonical: https://ross.abutalabs.com/products/nnunet
Language: Python
License: Apache-2.0
License Family: permissive
Topics: segmentation
Last push: 2026-07-23T06:09:30+00:00

## Health v2 (maintenance only)
Score: 65/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 94, release rhythm 8, longevity 100
- inputs: {"age_days": 2695, "days_push": 41, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 8829, forks 2468 (observed 2026-08-28T04:10:25.928714+00:00)

## What it is
nnU-Net is a self-configuring deep learning framework for semantic image segmentation that automatically adapts preprocessing, U-Net architecture, and training to a given dataset. It provides an end-to-end workflow from data preparation through training to inference, primarily targeting biomedical imaging.

## Use cases
- segment medical images like CT and MRI scans
- train a segmentation model on my own biomedical dataset
- get a strong baseline for a new segmentation method
- run automatic preprocessing and training for 2D and 3D segmentation
- participate in biomedical segmentation challenges
- fine-tune segmentation models from self-supervised checkpoints

## When to choose
- you need state-of-the-art biomedical or medical image segmentation with minimal tuning
- you want an end-to-end pipeline from raw data to trained model and inference
- you need a strong baseline for segmentation research on non-natural images

## When to avoid
- you need general natural-image segmentation with pretrained models
- you want a lightweight inference-only tool without training infrastructure
- your task is classification or detection rather than semantic segmentation

## Facets
- artifact type: framework
- maturity: stable
- function: machine-learning, deep-learning, image-processing
- domain: deep-learning, healthcare, machine-learning
- platform: python, cross-platform
- tags: semantic-segmentation, medical-imaging, u-net, self-configuring, pytorch, biomedical, gpu

## Member repositories
- MIC-DKFZ/nnUNet (main) score 65

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:10:25.928714+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 2026-08-29T17:24:49.799875+00:00, confidence not recorded.
  - readme: https://github.com/MIC-DKFZ/nnUNet (fetched 2026-08-28T04:10:25.928714+00:00, sha 94bc4feda1cf)
  - registry_pypi: https://pypi.org/pypi/nnunet/json (fetched 2026-08-29T08:25:13.494718+00:00, sha 885fb45ee5ee)
- Data as of 2026-08-30T08:39:29.467469+00:00.