#1818th of 50 in Data and scraping for AI
cvat
Self-hosted annotation tool for image, video, audio and 3D point cloud datasets
- Stars
- 17.1k
- License
- MIT
- Last commit
- Nov 2026
- Last release
- Nov 2026
- Language
- Python
15,560 on 21 Apr 202617,113 on 10 Nov 2026
From GitHub's daily star counts.
Checked on GitHub.
Overview
CVAT is a web-based labeling platform for computer vision datasets. It supports boxes, polygons, masks, keypoints and cuboids on images, video, audio and point clouds, organized into projects, tasks and jobs with roles and review. Auto-annotation runs through Nuclio serverless functions (SAM, YOLO v7, RetinaNet and others), and data exports in 20+ formats such as COCO and YOLO.
Who it is for: ML teams that need to label and manage computer vision training data in-house
Strengths
- Covers image, video, audio and 3D point cloud annotation in one tool
- Exports and imports 20+ formats including COCO, YOLO, Pascal VOC and KITTI
- Python SDK, CLI and REST API for scripting task creation and exports
- Multi-user organizations with roles, task assignment, comments and review workflows
Weaknesses
- Auto-annotation needs a separate Nuclio serverless stack and manual nuctl function deployment
- Built-in SAM 2/SAM 3 labeling, SSO and quality control UI are paid-only
- Serverless model code may use third-party assets with non-commercial licenses
- Safari is unsupported and Firefox has caveats; testing targets Chromium browsers
What it needs
- Docker + Compose
- Needs Docker Engine, Docker Compose, Git, Nuclio (for auto-annotation), Grafana (for analytics)
- Models: SAM, IOG, RetinaNet R101, HRNet32, TransT
- port 8080
Quick answers
Is cvat open source?
Yes. Its code is on GitHub under the MIT license.
Can you run cvat with Docker?
Yes. Its repository has a Docker setup: a Dockerfile, a Compose file or a published image.
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