# CASIA-LMC-Lab/FastSAM

Fast Segment Anything

Repository: https://github.com/CASIA-LMC-Lab/FastSAM
Canonical: https://ross.abutalabs.com/products/fastsam
Language: Python
License: AGPL-3.0
License Family: copyleft
Last push: 2024-07-30T13:35:06+00:00

## Health v2 (maintenance only)
Score: 19/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 8, longevity 83
- inputs: {"age_days": 1170, "days_push": 764, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 8401, forks 760 (observed 2026-08-28T04:10:20.934501+00:00)

## What it is
FastSAM is a CNN-based Segment Anything Model trained on only 2% of the SA-1B dataset, achieving comparable segmentation performance to SAM at 50x faster run-time speed. It supports promptable segmentation via text, points, and boxes, and is integrated into the Ultralytics YOLOv8 model hub.

## Use cases
- segment all objects in an image quickly
- fast alternative to Meta's Segment Anything Model
- segment objects with point or box prompts
- text-prompted image segmentation
- real-time instance segmentation on GPU
- generate segmentation masks for downstream vision tasks

## When to choose
- you need SAM-like segmentation but with much lower latency
- you want a lightweight CNN segmentation model deployable with TensorRT or Ultralytics
- you need promptable segmentation (points, boxes, text) in a Python pipeline

## When to avoid
- you need the highest possible segmentation accuracy regardless of speed
- you require a transformer-based SAM with the full SA-1B training
- your project cannot use AGPL-3.0 licensed code

## Facets
- artifact type: library
- maturity: active
- function: machine-learning, computer-vision, image-processing, deep-learning
- domain: computer-vision, image-processing, machine-learning, deep-learning
- platform: python, cross-platform
- tags: segment-anything, image-segmentation, yolov8, fastsam, promptable-segmentation, gpu

## Member repositories
- CASIA-LMC-Lab/FastSAM (main) score 19

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:10:20.934501+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 2026-08-29T17:27:18.835348+00:00, confidence not recorded.
  - readme: https://github.com/CASIA-LMC-Lab/FastSAM (fetched 2026-08-28T04:10:20.934501+00:00, sha 8b4aafa17efe)
- Data as of 2026-08-30T08:39:29.467469+00:00.