# tangzhenyu/SemanticSegmentation_DL

Resources of semantic segmantation based on Deep Learning model

Repository: https://github.com/tangzhenyu/SemanticSegmentation_DL
Canonical: https://ross.abutalabs.com/products/semanticsegmentation_dl
Language: Jupyter Notebook
License Family: other
Last push: 2021-03-10T00:32:08+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3285, "days_push": 2003, "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 1102, forks 312 (observed 2026-08-28T04:03:35.824502+00:00)

## What it is
A curated collection of papers, survey articles, and dataset links for semantic segmentation with deep learning, plus some Jupyter Notebook implementations. It serves primarily as a reference list rather than a production software tool.

## Use cases
- find datasets for training a semantic segmentation model
- survey deep learning papers on semantic segmentation
- get started learning image segmentation with deep learning
- compare segmentation benchmarks like Cityscapes, ADE20K, and VOC2012
- find reference implementations of segmentation models in notebooks

## When to choose
- you need a curated index of segmentation datasets and papers
- you are researching or learning semantic segmentation techniques
- you want quick links to benchmarks like PASCAL VOC, Cityscapes, or COCO-Stuff

## When to avoid
- you need a maintained, production-ready segmentation library
- you require a licensed, supported codebase for deployment
- you need actively updated state-of-the-art model implementations

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: computer-vision, image-processing, machine-learning, deep-learning
- domain: computer-vision, machine-learning, deep-learning, autonomous-vehicles, tutorials
- platform: python, cross-platform
- tags: semantic-segmentation, awesome-list, datasets, papers, jupyter-notebook, survey

## Member repositories
- tangzhenyu/SemanticSegmentation_DL (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:35.824502+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 2026-08-30T06:45:39.805670+00:00, confidence not recorded.
  - readme: https://github.com/tangzhenyu/SemanticSegmentation_DL (fetched 2026-08-28T04:03:35.824502+00:00, sha 2abefb1e3739)
- Data as of 2026-08-30T08:39:29.467469+00:00.