# open-mmlab/mmsegmentation

OpenMMLab Semantic Segmentation Toolbox and Benchmark.

Repository: https://github.com/open-mmlab/mmsegmentation
Canonical: https://ross.abutalabs.com/products/mmsegmentation
Homepage: https://mmsegmentation.readthedocs.io/en/main/
Language: Python
License: Apache-2.0
License Family: permissive
Topics: semantic-segmentation, pytorch, pspnet, deeplabv3, transformer, swin-transformer, realtime-segmentation, vessel-segmentation, retinal-vessel-segmentation, image-segmentation, medical-image-segmentation
Last push: 2024-08-13T08:53:34+00:00

## Health v2 (maintenance only)
Score: 23/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 8, longevity 100
- inputs: {"age_days": 2271, "days_push": 750, "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 9930, forks 2853 (observed 2026-08-28T04:10:38.835222+00:00)

## What it is
MMSegmentation is a PyTorch-based toolbox and benchmark for semantic segmentation, part of the OpenMMLab ecosystem. It provides implementations of many segmentation models (e.g., PSPNet, DeepLabV3, SegFormer, Swin Transformer), training/evaluation pipelines, and a large model zoo.

## Use cases
- train a semantic segmentation model on a custom dataset
- run image segmentation inference with pretrained models
- benchmark segmentation architectures like DeepLabV3 and PSPNet
- segment retinal vessels or medical images
- fine-tune a transformer-based segmentation model like SegFormer
- evaluate segmentation models on standard datasets like ADE20K or Cityscapes

## When to choose
- you need a well-tested PyTorch framework for semantic segmentation with many ready-made models
- you want config-driven training and evaluation with a large model zoo
- you need strong baselines for image or medical image segmentation research

## When to avoid
- you need a lightweight inference-only solution without the OpenMMLab dependency stack
- you work outside PyTorch or need real-time deployment on edge devices without conversion tooling
- you want a simple one-off segmentation script rather than a full training framework

## Facets
- artifact type: library
- maturity: stable
- function: machine-learning, deep-learning, image-processing, computer-vision
- domain: computer-vision, image-processing, deep-learning, machine-learning, healthcare
- platform: python, windows
- tags: semantic-segmentation, pytorch, deeplabv3, pspnet, swin-transformer, model-zoo, benchmark, medical-imaging, openmmlab, linux, macos, gpu

## Member repositories
- open-mmlab/mmsegmentation (main) score 23

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:10:38.835222+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 2026-08-29T17:20:17.995098+00:00, confidence not recorded.
  - readme: https://github.com/open-mmlab/mmsegmentation (fetched 2026-08-28T04:10:38.835222+00:00, sha f2ef147f39a3)
  - homepage: https://mmsegmentation.readthedocs.io/en/main/ (fetched 2026-08-29T08:20:02.854928+00:00, sha e9d87db66bec)
  - registry_pypi: https://pypi.org/pypi/mmsegmentation/json (fetched 2026-08-29T08:20:02.863825+00:00, sha 7f245f26a385)
- Data as of 2026-08-30T08:39:29.467469+00:00.