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CVAT

Collaborative video and image annotation platform for computer vision

Data Labeling & Annotation

CVAT provides an interactive web interface to label images and videos, supporting dozens of formats, Docker deployment, SDK, CLI, and integrations with Roboflow and HuggingFace for scalable data annotation.

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Overview

Overview

CVAT (Computer Vision Annotation Tool) is a web‑based platform that lets teams label images and videos through an interactive UI. It supports more than 30 import and export formats—including COCO, YOLO, PASCAL VOC, and MOT—so datasets can be created or converted without manual preprocessing.

Deployment & Extensibility

The tool can be used instantly via the free online service at cvat.ai (limited to 10 tasks and 500 MB of data) or self‑hosted with pre‑built Docker images for server and UI, enabling on‑premise security and scalability. Automation is possible through a RESTful API, a Python SDK, and a command‑line client, while integrations with Roboflow and HuggingFace streamline auto‑annotation and model‑in‑the‑loop workflows. Enterprise options add SSO, LDAP, and upcoming analytics for larger teams.

A vibrant community contributes plugins, documentation, and Docker updates, while the project offers paid enterprise support with 24‑hour SLA, training, and dedicated assistance. This ecosystem makes CVAT suitable for both research prototypes and production‑grade annotation pipelines.

Highlights

Supports 30+ import and export annotation formats
Web‑based UI with real‑time collaboration
Docker images for quick self‑hosted deployment
SDK, CLI, and API for automation and integration

Pros

  • Extensive format compatibility
  • Scalable self‑hosted deployment via Docker
  • Rich automation through SDK/CLI
  • Active community and enterprise support

Considerations

  • Online free tier limits tasks and data size
  • Analytics features not yet available
  • Enterprise features require paid support
  • Initial setup may need Docker/Kubernetes knowledge

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Looking for a hosted option? These are the services engineering teams benchmark against before choosing open source.

Fit guide

Great for

  • Teams building custom computer‑vision datasets
  • Researchers needing reproducible annotation pipelines
  • Enterprises requiring on‑premise data security
  • Developers integrating annotation into ML workflows

Not ideal when

  • One‑off casual users needing only a few labels
  • Projects that cannot allocate Docker infrastructure
  • Users requiring built‑in advanced analytics today
  • Organizations without technical staff to manage self‑hosted deployment

How teams use it

Semantic segmentation dataset creation

Produce high‑quality pixel masks for training segmentation models

Video object tracking

Annotate bounding boxes across frames to generate training data for tracking algorithms

Model fine‑tuning with auto‑annotations

Use Roboflow integration to generate initial labels and refine them manually

Dataset conversion

Import legacy PASCAL VOC labels and export to COCO format for downstream pipelines

Tech snapshot

Python44%
TypeScript40%
JavaScript11%
Mustache2%
SCSS2%
Open Policy Agent1%

Tags

labeling-toolcomputer-visionobject-detectionimage-labelingpytorchimage-classificationvideo-annotationdatasetcomputer-vision-annotationsemantic-segmentationannotationimage-labelling-toolimage-annotationannotationslabelingboundingboxdeep-learningtensorflowannotation-toolimagenet

Frequently asked questions

What are the limits of the free online version?

The free tier allows up to 10 annotation tasks and 500 MB of uploaded data per user.

Can CVAT be self‑hosted?

Yes, pre‑built Docker images for the server and UI enable on‑premise deployment, with optional Kubernetes support for larger installations.

Which annotation formats does CVAT support?

CVAT can import and export more than 30 formats, including COCO, YOLO, PASCAL VOC, MOT, and many others.

Is enterprise support available?

Paid enterprise support offers SSO, LDAP, dedicated assistance, 24‑hour SLA, and upcoming analytics features.

How can I automate labeling with CVAT?

Automation is possible via the REST API, Python SDK, and the cvat‑cli command‑line tool, as well as integrations with Roboflow and HuggingFace.

Project at a glance

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LicenseMIT
Repo age7 years old
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