# yangchris11/samurai

Official repository of "SAMURAI: Adapting Segment Anything Model for Zero-Shot Visual Tracking with Motion-Aware Memory"

Repository: https://github.com/yangchris11/samurai
Canonical: https://ross.abutalabs.com/products/samurai
Homepage: https://yangchris11.github.io/samurai/
Language: Python
License: Apache-2.0
License Family: permissive
Last push: 2025-03-18T20:49:46+00:00

## Health v2 (maintenance only)
Score: 27/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 12, release rhythm 35, longevity 47
- inputs: {"age_days": 665, "days_push": 533, "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 7112, forks 502 (observed 2026-08-28T04:09:55.989647+00:00)

## What it is
SAMURAI is the official implementation of a zero-shot visual object tracker built on top of Segment Anything Model 2 (SAM 2), using a motion-aware memory selection mechanism to improve tracking without retraining. It runs in real time and achieves state-of-the-art zero-shot results on benchmarks like LaSOT, LaSOT-ext, GOT-10k, NFS, and OTB-2015.

## Use cases
- track and segment an object across a video without training
- zero-shot visual object tracking on benchmark datasets
- segment fast-moving or occluding objects in crowded scenes
- run SAM 2 with motion-aware memory for better mask selection
- evaluate a tracker on LaSOT or GOT-10k
- multi-GPU batch inference on videos

## When to choose
- you need training-free, zero-shot object tracking with segmentation masks
- you want strong tracking of fast-moving or self-occluding objects
- you are benchmarking trackers on standard VOT datasets
- you already use SAM 2 and need better temporal consistency

## When to avoid
- you need a lightweight CPU-only tracker
- you require a production-ready API or GUI rather than research scripts
- you need fully supervised fine-tuned tracking accuracy on a specific domain
- you need real-time tracking on edge devices with limited GPU resources

## Facets
- artifact type: library
- maturity: active
- function: computer-vision, machine-learning, image-processing, video-processing
- domain: computer-vision, machine-learning, deep-learning, artificial-intelligence
- platform: python
- tags: visual-object-tracking, segment-anything, sam2, zero-shot, motion-aware-memory, video-segmentation, research-code, gpu, linux

## Member repositories
- yangchris11/samurai (main) score 27

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:55.989647+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 2026-08-29T17:40:06.907416+00:00, confidence not recorded.
  - readme: https://github.com/yangchris11/samurai (fetched 2026-08-28T04:09:55.989647+00:00, sha 7676dd9d3273)
  - homepage: https://yangchris11.github.io/samurai/ (fetched 2026-08-29T08:35:40.933492+00:00, sha 930b0535ebeb)
- Data as of 2026-08-30T08:39:29.467469+00:00.