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cvat-ai/cvat

CVAT is a leading data annotation platform for image, video, audio, and 3D datasets. It offers open-source, cloud, and enterprise products, as well as labeling services, with AI-assisted labeling, quality assurance, team collaboration, analytics, and developer APIs. observed · 2026-10-11

github.com/cvat-ai/cvat · homepage · Python · MIT (permissive) observed · 2026-10-11

Health v2 · maintenance only

95/100

  • Activity 99
  • Release rhythm 86
  • Longevity 100
How is this computed?

round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-10-21. Adoption (stars, forks) is never an input.

  • gap_med: 8
  • age_days: 3035
  • days_rel: 14
  • days_push: 11
  • n_releases_24m: 70

Full methodology

Adoption not part of the score

16896 stars · 3947 forks observed · 2026-10-11

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

CVAT (Computer Vision Annotation Tool) is an open-source, self-hosted platform for annotating images, videos, and 3D point clouds to build high-quality training datasets for computer vision AI. It supports bounding boxes, polygons, masks, skeletons, and tracks, with AI-assisted labeling, team collaboration, quality assurance workflows, cloud storage integration, and developer SDKs/APIs.

Use cases

  • annotate images with bounding boxes for object detection training
  • label video frames with object tracking
  • create semantic segmentation masks for datasets
  • annotate 3D point clouds from lidar
  • run AI-assisted auto-labeling with SAM or custom models
  • manage a team of annotators with review and QA workflows
  • self-host a data labeling platform with full data control
  • export annotations in formats like COCO, YOLO, or Pascal VOC

When to choose

  • you need a battle-tested, widely adopted annotation tool for computer vision datasets
  • you must keep sensitive visual data on your own infrastructure
  • you need multi-user collaboration, roles, and review pipelines for labeling teams
  • you want to plug in your own ML models to accelerate labeling
  • you need image, video, and 3D point cloud annotation in one platform

When to avoid

  • you only need simple text or tabular data labeling rather than visual data
  • you want a lightweight single-user desktop annotation tool without server deployment
  • you need audio annotation, which is not yet supported
  • you cannot operate Docker-based multi-service deployments

Facets

application · maturity active

image-processing computer-vision machine-learning data-science web-framework api-framework self-hosted developer-tools computer-vision machine-learning data-science image-processing artificial-intelligence deep-learning self-hosted python cross-platform cloud data-annotation labeling image-annotation video-annotation 3d-annotation bounding-boxes semantic-segmentation object-detection pose-estimation object-tracking point-clouds dataset-management ai-assisted-labeling quality-assurance team-collaboration sam nuclio-serverless sdk mit-license video docker web-server

10 sources

Member repositories

RepositoryRoleHealth v2
cvat-ai/cvatmain95

For agents

markdown · JSON · MCP: product_card(name="cvat-ai/cvat")

Data as of 2026-10-19T08:05:51.212177+00:00 · Report a problem