# mrgloom/awesome-semantic-segmentation

:metal: awesome-semantic-segmentation

Repository: https://github.com/mrgloom/awesome-semantic-segmentation
Canonical: https://ross.abutalabs.com/products/awesome-semantic-segmentation
License Family: other
Topics: semantic-segmentation, benchmark, evaluation, deeplearning
Last push: 2021-05-08T13:40:11+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": 3987, "days_push": 1943, "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 10847, forks 2462 (observed 2026-08-28T04:10:44.497137+00:00)

## What it is
A curated awesome-list of resources for semantic segmentation in deep learning, including papers, network architectures (U-Net, etc.), implementations, benchmarks, and evaluation tools. It is a reference catalog rather than runnable software.

## Use cases
- find semantic segmentation model implementations
- compare segmentation architectures like U-Net
- find benchmarks for image segmentation
- learn about deep learning segmentation papers
- find pretrained segmentation models in PyTorch or Keras

## When to choose
- researching state-of-the-art semantic segmentation methods
- looking for open-source implementations of segmentation networks
- building a survey or comparison of segmentation models

## When to avoid
- you need a ready-to-use segmentation library rather than a link list
- you need actively maintained code with releases and support

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, computer-vision, image-processing, benchmarking
- domain: deep-learning, computer-vision, machine-learning, awesome-lists
- platform: cross-platform
- tags: awesome-list, semantic-segmentation, curated-list, deep-learning, papers, benchmarks

## Member repositories
- mrgloom/awesome-semantic-segmentation (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:10:44.497137+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 2026-08-29T17:17:30.027515+00:00, confidence not recorded.
  - readme: https://github.com/mrgloom/awesome-semantic-segmentation (fetched 2026-08-28T04:10:44.497137+00:00, sha 4d7a00f671d1)
- Data as of 2026-08-30T08:39:29.467469+00:00.