# msracver/FCIS

Fully Convolutional Instance-aware Semantic Segmentation

Repository: https://github.com/msracver/FCIS
Canonical: https://ross.abutalabs.com/products/fcis
Language: Cuda
License: MIT
License Family: permissive
Last push: 2021-09-27T02:16:42+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": 3618, "days_push": 1802, "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 1561, forks 403 (observed 2026-08-28T04:05:03.817186+00:00)

## What it is
FCIS is the official MXNet implementation of the CVPR 2017 paper 'Fully Convolutional Instance-aware Semantic Segmentation', which won first place in the COCO 2016 segmentation challenge. It provides a fast, end-to-end fully convolutional framework that jointly performs instance mask estimation and categorization.

## Use cases
- segment individual object instances in images
- reproduce COCO 2016 challenge-winning instance segmentation results
- compare instance segmentation baselines against Mask R-CNN
- research fully convolutional approaches to instance-aware segmentation
- train and evaluate instance segmentation models on COCO with MXNet

## When to choose
- you need a fast, fully convolutional instance segmentation baseline
- you are reproducing or building on the FCIS CVPR 2017 paper
- your stack is already MXNet-based and you need instance segmentation

## When to avoid
- you need actively maintained code or modern framework support (PyTorch/TensorFlow)
- you want state-of-the-art accuracy with modern tricks like FPN and ROIAlign (Mask R-CNN style)
- you need production-ready segmentation with long-term support

## Facets
- artifact type: library
- maturity: maintenance
- function: machine-learning, deep-learning, computer-vision, image-processing
- domain: computer-vision, deep-learning, machine-learning, image-processing
- platform: python
- tags: instance-segmentation, semantic-segmentation, mxnet, coco, cvpr-2017, research-code, microsoft-research, linux, gpu, cuda

## Member repositories
- msracver/FCIS (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:03.817186+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 2026-08-30T04:29:44.576787+00:00, confidence not recorded.
  - readme: https://github.com/msracver/FCIS (fetched 2026-08-28T04:05:03.817186+00:00, sha f59611ba98c6)
- Data as of 2026-08-30T08:39:29.467469+00:00.