# zijundeng/pytorch-semantic-segmentation

PyTorch for Semantic Segmentation

Repository: https://github.com/zijundeng/pytorch-semantic-segmentation
Canonical: https://ross.abutalabs.com/products/pytorch-semantic-segmentation
Language: Python
License: MIT
License Family: permissive
Topics: semantic-segmentation, deep-learning, fully-convolutional-networks, pytorch
Last push: 2019-10-25T09:05:41+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": 3379, "days_push": 2504, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1735, forks 390 (observed 2026-08-28T04:05:29.119300+00:00)

## What it is
A PyTorch library providing implementations of popular semantic segmentation models such as FCN, U-Net, SegNet, PSPNet, GCN, and DUC/HDC. It includes training and testing pipelines for these models.

## Use cases
- train semantic segmentation models in pytorch
- implement FCN, U-Net, or SegNet architectures
- benchmark segmentation models on custom datasets
- learn how semantic segmentation networks are built
- fine-tune pretrained segmentation models

## When to choose
- you need reference implementations of classic segmentation architectures in PyTorch
- you want a simple training/testing pipeline for segmentation research
- you are studying fully convolutional networks and encoder-decoder models

## When to avoid
- you need state-of-the-art or actively maintained segmentation tooling
- you require modern PyTorch version compatibility
- you need production-ready segmentation deployment

## Facets
- artifact type: library
- maturity: maintenance
- function: deep-learning, machine-learning, image-processing
- domain: computer-vision, deep-learning, machine-learning
- platform: python
- tags: semantic-segmentation, pytorch, fcn, unet, segnet, pspnet, computer-vision, gpu

## Member repositories
- zijundeng/pytorch-semantic-segmentation (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:29.119300+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 2026-08-30T03:30:50.887775+00:00, confidence not recorded.
  - readme: https://github.com/zijundeng/pytorch-semantic-segmentation (fetched 2026-08-28T04:05:29.119300+00:00, sha e578f5dafedd)
- Data as of 2026-08-30T08:39:29.467469+00:00.