# fudan-zvg/Semantic-Segment-Anything

Automated dense category annotation engine that serves as the initial semantic labeling for the Segment Anything dataset (SA-1B).

Repository: https://github.com/fudan-zvg/Semantic-Segment-Anything
Canonical: https://ross.abutalabs.com/products/semantic-segment-anything
Language: Python
License: Apache-2.0
License Family: permissive
Last push: 2023-06-07T11:54:19+00:00

## Health v2 (maintenance only)
Score: 30/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 88
- inputs: {"age_days": 1242, "days_push": 1183, "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 2301, forks 142 (observed 2026-08-28T04:06:35.492007+00:00)

## What it is
Semantic Segment Anything (SSA) is a Python framework that adds semantic category prediction to the Segment Anything Model (SAM) by combining SAM's precise masks with existing semantic segmenters like Segformer, Oneformer, and CLIPSeg. It also includes SSA-engine, an automated dense open-vocabulary annotation engine used to generate the initial semantic labels for the SA-1B segmentation dataset.

## Use cases
- add semantic category labels to SAM segmentation masks
- automatically annotate large image datasets with dense semantic categories
- generate open-vocabulary segmentation annotations without retraining SAM
- build a semantic segmentation dataset from SA-1B masks
- combine multiple segmenters with SAM for better mask boundaries
- reduce manual annotation costs for segmentation datasets

## When to choose
- you need semantic categories on top of SAM's class-agnostic masks
- you want to auto-label a large image dataset for segmentation training
- you want to plug existing segmenters into SAM without fine-tuning its weights

## When to avoid
- you need a simple off-the-shelf semantic segmenter with no SAM dependency
- you require real-time segmentation on edge devices
- you need actively maintained software with frequent updates

## Facets
- artifact type: library
- maturity: maintenance
- function: computer-vision, image-processing, machine-learning, data-generation
- domain: computer-vision, image-processing, machine-learning, artificial-intelligence
- platform: python
- tags: segment-anything, semantic-segmentation, sam, annotation-engine, open-vocabulary, dataset-labeling, linux, gpu

## Member repositories
- fudan-zvg/Semantic-Segment-Anything (main) score 30

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:35.492007+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 2026-08-30T02:40:12.504421+00:00, confidence not recorded.
  - readme: https://github.com/fudan-zvg/Semantic-Segment-Anything (fetched 2026-08-28T04:06:35.492007+00:00, sha 1ccfe37c8a14)
- Data as of 2026-08-30T08:39:29.467469+00:00.