# wkentaro/pytorch-fcn

PyTorch Implementation of Fully Convolutional Networks. (Training code to reproduce the original result is available.)

Repository: https://github.com/wkentaro/pytorch-fcn
Canonical: https://ross.abutalabs.com/products/pytorch-fcn
Language: Python
License: MIT
License Family: permissive
Topics: pytorch, computer-vision, deep-learning, semantic-segmentation, convolutional-networks, fcn, fcn8s
Archived: true
Last push: 2023-02-05T15:40:40+00:00

## Health v2 (maintenance only)
Score: 10/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3485, "days_push": 1305, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, archived
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1777, forks 470 (observed 2026-08-28T04:05:35.146634+00:00)

## What it is
A PyTorch implementation of Fully Convolutional Networks (FCN) for semantic segmentation, including training code that reproduces the original paper's results on PASCAL VOC. It provides FCN32s, FCN16s, FCN8s, and FCN8sAtOnce models with pretrained weights.

## Use cases
- train a semantic segmentation model on pascal voc
- implement fully convolutional networks in pytorch
- download pretrained fcn8s weights for segmentation
- reproduce original fcn paper results
- pixel-wise image segmentation with deep learning
- compare fcn32s fcn16s fcn8s mean iou accuracy

## When to choose
- you need the classic FCN architecture in PyTorch with verified reproduction of original results
- you want pretrained FCN models for PASCAL VOC segmentation
- you need a reference baseline for semantic segmentation research

## When to avoid
- you need state-of-the-art segmentation models like DeepLab or Mask2Former
- you want a actively developed library with frequent updates
- you need segmentation for non-VOC datasets without writing your own training pipeline

## 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, fully-convolutional-networks, pytorch, fcn8s, fcn32s, fcn16s, voc, training-code, gpu

## Member repositories
- wkentaro/pytorch-fcn (main) score 10

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:35.146634+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 2026-08-30T03:25:14.921886+00:00, confidence not recorded.
  - readme: https://github.com/wkentaro/pytorch-fcn (fetched 2026-08-28T04:05:35.146634+00:00, sha c52d14e51e6f)
- Data as of 2026-08-30T08:39:29.467469+00:00.