cvat README

The project's README from github.com/cvat-ai/cvat. As one markdown file: README.md.

CVAT Community header

CVAT: Computer Vision Annotation Tool

Release GitHub stars License CI server pulls ui pulls CVAT Online CVAT Enterprise Status Discord Docs

Website · Docs · Changelog · Tutorials · Academy · Blog

What is CVAT Community?

CVAT Community is the free, self-hosted open-source edition of CVAT — one of the most widely used data annotation platforms for building high-quality visual datasets for computer vision and visual AI. Since 2018, CVAT has become one of the best-known data annotation tools in computer vision, with a large open-source community, millions of Docker pulls, and broad adoption across research and production AI teams.

CVAT Community supports image, video, and 3D annotation, dataset management, team collaboration, cloud storage integration, developer-friendly SDKs and APIs, and gives your team full control over your data and annotation infrastructure. The platform serves as the foundation of CVAT Online and CVAT Enterprise, and is actively maintained by the CVAT engineering team.

Why teams choose CVAT Community:

This repository contains the source code and deployment assets for CVAT Community.

For a fully managed setup, annotation services, or enterprise features, see CVAT Online, CVAT Enterprise and CVAT Labeling Services.

Getting Started

💡 Want to explore CVAT before deploying anything? Try CVAT Online (Free plan) directly in your browser. Feature availability and usage limits vary by plan; see CVAT Online pricing for details.

Installation

Prerequisites:

💡 CVAT is primarily tested with Chromium-based browsers (Google Chrome, Microsoft Edge). Firefox may work with some caveats; Safari/WebKit is not supported.

1. Start the default stack

Clone the repository and launch the services.

git clone https://github.com/cvat-ai/cvat
cd cvat

# Optional: set your IP or domain
# export CVAT_HOST=your-ip-or-domain

docker compose up -d

2. Create an admin account

docker exec -it cvat_server bash -ic 'python3 ~/manage.py createsuperuser'

See the Installation Guide for full instructions and OS-specific setup.

3. Sign in and start labeling

Learn more about annotation tools and workflows in the CVAT Documentation or take our free course – CVAT Academy.

For alternative deployments (AWS, Kubernetes, external PostgreSQL, backups, upgrades), see the Deployment Guides.

Key Capabilities

Advanced capabilities such as advanced project analytics, quality control UI, built-in auto-labeling with SAM 2 and SAM 3, AI agents, SSO, and more are available in CVAT Online paid plans (Solo, Team) and CVAT Enterprise.

Developer Tools

CVAT is designed for automation. Beyond the Web UI, you can integrate it into your pipelines using:

Data and Formats

CVAT Community supports image, video, and 3D (point cloud) annotation workflows. You can move data in and out using 20+ industry-standard formats: CVAT (XML), COCO (JSON), YOLO (TXT), Ultralytics YOLO (TXT/YAML), Pascal VOC (XML), KITTI (TXT), MOT (TXT), and more.

Full list of supported formats.

ML and AI Models

CVAT Community supports automatic annotation via pre-built serverless models powered by Nuclio, covering detection, segmentation, pose estimation, and tracking:

Model Framework Type
Segment Anything (SAM) PyTorch Interactor
Inside-Outside Guidance (IOG) PyTorch Interactor
RetinaNet R101 PyTorch Detector
HRNet32 Whole Body Pose PyTorch Pose Estimation
TransT PyTorch Tracker
YOLO v7 ONNX Detector
Mask RCNN Inception ResNet v2 OpenVINO Detector
Face Detection 0205 OpenVINO Detector
Faster RCNN Inception v2 TensorFlow Detector

To enable automatic annotation, add the serverless component to your deployment:

docker compose -f docker-compose.yml -f components/serverless/docker-compose.serverless.yml up -d

This starts the serverless infrastructure. To make models available in CVAT, install nuctl and deploy the functions you need, for example SAM or YOLO, as described in the Automatic Annotation Guide.

Which CVAT edition should I choose?

For detailed plan limits and feature availability, see CVAT Online pricing, CVAT Enterprise, and Labeling Services.

Support

For dedicated support, SLAs, or advanced deployments, consider CVAT Enterprise.

Contributing

We welcome all contributions: bug reports, documentation fixes, integrations, and code.

Security

License

CVAT Community is released under the MIT License.

Additional Resources

For the latest product releases, feature walkthroughs, and all things CVAT see:

CVAT Blog CVAT Academy Case Studies YouTube LinkedIn

cvat (cvat-ai/cvat)

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16.9K ·GitHub
What is this?CVAT is an open-source, self-hosted data annotation platform for building labeled datasets from images, video, and 3D point clouds, used for computer vision and visual AI projects.
Is this a web app I host myself, or a library I import into code?It is a full web application (server plus browser UI) that you deploy yourself via Docker Compose, not a code library — though it also ships a Python SDK and a CLI for scripting your workflows.
How do I get it running?Clone the repository, launch the default Docker Compose stack to start the server and UI, create a superuser account, then open the app in a browser to create a project and upload data for labeling.
What do I need installed before I start it?Just Docker Engine, Docker Compose, and Git — the server, database, and UI all run inside the containers.
Any catches with browser support or features?CVAT is mainly tested on Chromium-based browsers; Firefox may have caveats and Safari/WebKit isn't supported. Built-in SAM 2/3 auto-labeling, the quality-control UI, and SSO are reserved for the paid Online/Enterprise tiers, not the free Community edition.
Does any of my data leave my servers?No — running CVAT Community yourself keeps all your data and uploads inside your own infrastructure.
What are the license terms?The Community edition's core is MIT-licensed, but code under /serverless and some bundled ML model assets may carry separate, sometimes non-commercial, licenses.
What annotation types does CVAT support?Bounding boxes, polygons, masks, keypoints, cuboids, and tags, applied across images, video, and 3D point cloud data.
Can CVAT auto-label my data using ML models?Yes, it ships pre-built serverless models (Segment Anything, YOLOv7, RetinaNet, Mask RCNN, face and pose detectors) via Nuclio, and you can deploy more detectors or trackers yourself.
What import/export formats does CVAT work with?Over 20 formats, including CVAT XML, COCO, YOLO, Ultralytics YOLO, Pascal VOC, KITTI, and MOT.
What's the difference between CVAT Community, Online, and Enterprise?Community is the free, self-hosted MIT-licensed core covered by this repo; Online is a managed hosted version with paid plans for extra capacity; Enterprise adds SSO, enterprise support, and SLAs for running CVAT in your own cloud.
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