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Getting started

1 - CVAT Overview

The open-source tool for image and video annotation

Machine learning systems often struggle due to poor-quality data. Without effective tools, improving a model can be tough and inefficient.

CVAT.ai is a versatile tool for annotating images and videos, serving the computer vision community worldwide.

Our goal is to help developers, businesses, and organizations globally by using a Data-centric AI approach.

CVAT offers three versions:

See:

Tools and formats

CVAT stands as a comprehensive tool for image and video annotation, essential for various computer vision tasks.

It emphasizes user-friendliness, adaptability, and compatibility with a range of formats and tools.

Supported formats

CVAT’s supports the following formats:

  • For 3D: .pcd, .bin
  • For image: everything supported by the Python Pillow library, including formats like JPEG, PNG, BMP, GIF, PPMand TIFF.
  • For video: all formats, supported by ffmpeg, including MP4, AVI, and MOV.

For annotation export and import formats, see Export annotations and data from CVAT

Annotation tools

CVAT offers a wide range of annotation tools, each catering to different aspects of image and video labeling:

Annotation Tool Use Cases
3D Object Annotation Ideal for projects that require depth perception and volume estimation, like autonomous vehicle training.
Attribute Annotation Mode Useful for adding detailed information to objects, like color, size, or other specific characteristics.
Annotation with Rectangles Best for simple object detection where objects have a box-like shape, such as detecting windows in a building.
Annotation with Polygons Suited for complex shapes in images, like outlining geographical features in maps or detailed product shapes.
Annotation with Polylines Great for annotating linear objects like roads, pathways, or limbs in pose estimation.
Annotation with Ellipses Ideal for objects like plates, balls, or eyes, where a circular or oval annotation is needed.
Annotation with Cuboids Useful for 3D objects in 2D images, like boxes or furniture in room layouts.
Annotation with Skeletons Ideal for human pose estimation, animation, and movement analysis in sports or medical fields.
Annotation with Brush Tool Perfect for intricate and detailed annotations where precision is key, such as in medical imaging.
Annotation with Tags Useful for image and video classification tasks, like identifying scenes or themes in a dataset.

These tools make CVAT a versatile platform for a range of annotation needs, from basic labeling to complex, multidimensional tasks in advanced computer vision projects.

Automated labeling

CVAT has an automated labeling features, enhancing the annotation process significantly, potentially speeding it up by up to 10 times.

Note: For more information, see OpenCV and AI Tools

Below is a detailed table of the supported algorithms and the platforms they operate on:

Algorithm Name Category Framework CPU Support GPU Support
Segment Anything Interactor PyTorch ✔️ ✔️
Deep Extreme Cut Interactor OpenVINO ✔️
Faster RCNN Detector OpenVINO ✔️
Mask RCNN Detector OpenVINO ✔️
YOLO v3 Detector OpenVINO ✔️
YOLO v7 Detector ONNX ✔️ ✔️
Object Reidentification ReID OpenVINO ✔️
Semantic Segmentation for ADAS Detector OpenVINO ✔️
Text Detection v4 Detector OpenVINO ✔️
SiamMask Tracker PyTorch ✔️ ✔️
TransT Tracker PyTorch ✔️ ✔️
f-BRS Interactor PyTorch ✔️
HRNet Interactor PyTorch ✔️
Inside-Outside Guidance Interactor PyTorch ✔️
Faster RCNN Detector TensorFlow ✔️ ✔️
Mask RCNN Detector TensorFlow ✔️ ✔️
RetinaNet Detector PyTorch ✔️ ✔️
Face Detection Detector OpenVINO ✔️

Start here if you’re unsure where to begin with CVAT.

Cloud

Name Description
User Manual This comprehensive guide covers all CVAT tools available for work. It includes descriptions of all available tools, quality control methods, and procedures for importing and exporting data. This manual is relevant for both CVAT Cloud and Self-Hosted versions.
CVAT Complete Workflow Guide for Organizations This guide provides a comprehensive overview of using CVAT for collaboration in organizations.
Subscription Management Learn how to choose a plan, subscribe, and manage your subscription effectively.
XML Annotation Format Detailed documentation on the XML format used for annotations in CVAT essential for understanding data structure and compatibility.

Self-Hosted

Name Description
Self-hosted Installation Guide Start here to install self-hosted solution on your premises.
Dataset Management Framework Specifically for the Self-Hosted version, this framework and CLI tool are essential for building, transforming, and analyzing datasets.
Server API The CVAT server offers a HTTP REST API for interactions. This section explains how client applications, whether they are command line tools, browsers, or scripts, interact with CVAT through HTTP requests and responses.
Python SDK The CVAT SDK is a Python library providing access to server interactions and additional functionalities like data validation and serialization.
Command Line Tool This tool offers a straightforward command line interface for managing CVAT tasks. Currently featuring basic functionalities, it has the potential to develop into a more advanced administration tool for CVAT.
XML Annotation Format Detailed documentation on the XML format used for annotations in CVAT essential for understanding data structure and compatibility.
AWS Deployment Guide A step-by-step guide for deploying CVAT on Amazon Web Services, covering all necessary procedures and tips.
Frequently Asked Questions This section addresses common queries and provides helpful answers and insights about using CVAT.

Integrations

CVAT is a global tool, trusted and utilized by teams worldwide. Below is a list of key companies that contribute significantly to our product support or are an integral part of our ecosystem.

Note: If you’re using CVAT, we’d love to hear from you at contact@cvat.ai.

Integrated Service Available In Description
Human Protocol Cloud and Self-hosted Incorporates CVAT to augment annotation services within the Human Protocol framework, enhancing its capabilities in data labeling.
FiftyOne Cloud and Self-hosted An open-source tool for dataset management and model analysis in computer vision, FiftyOne is closely integrated with CVAT to enhance annotation capabilities and label refinement.
Hugging Face & Roboflow Cloud In CVAT Cloud, models from Hugging Face and Roboflow can be added to enhance computer vision tasks. For more information, see Integration with Hugging Face and Roboflow

License Information

CVAT includes the following licenses:

License Type Applicable To Description
MIT License Self-hosted This code is distributed under the MIT License, a permissive free software license that allows for broad use, modification, and distribution.
LGPL License (FFmpeg) Cloud and Self-hosted Incorporates LGPL-licensed components from the FFmpeg project. Users should verify if their use of FFmpeg requires additional licenses. CVAT.ai Corporation does not provide these licenses and is not liable for any related licensing fees.
Commercial License Self-hosted Enterprise For commercial use of the Enterprise solution of CVAT, a separate commercial license is applicable. This is tailored for businesses and commercial entities.
Terms of Use Cloud and Self-hosted Outlines the terms of use and confidential information handling for CVAT. Important for understanding the legal framework of using the platform.
Privacy Policy Cloud Our Privacy Policy governs your visit to https://cvat.ai and your use of https://app.cvat.ai, and explains how we collect, safeguard and disclose information that results from your use of our Service.

Get in touch

To get in touch, use one of the following channels:

Support Channel Applicable To Description
Discord Channel Cloud and Self-hosted A space for broader discussions, questions, and all things related to CVAT.
LinkedIn Cloud and Self-hosted Follow for company updates, news, and employment opportunities.
YouTube Channel Cloud and Self-hosted Find tutorials and screencasts about CVAT tools.
GitHub Issues Cloud and Self-hosted Report bugs or contribute to the ongoing development of CVAT.
Customer Support Channel Cloud (Paid Users) Exclusive support for CVAT.ai cloud paid users.
Commercial Support Inquiries Cloud and Self-hosted For direct commercial support inquiries, email contact@cvat.ai.

2 - CVAT Complete Workflow Guide for Organizations

Welcome to CVAT.ai, this page is the place to start your team’s annotation process using the Computer Vision Annotation Tool (CVAT).

This guide aims to equip your organization with the knowledge and best practices needed to use CVAT effectively.

We’ll walk you through every step of the CVAT workflow, from initial setup to advanced features.

See:

Workflow diagram

The workflow diagram presents an overview of the general process at a high level.

Workflow diagram

End-to-end workflow for Organizations

To use CVAT within your organization, please follow these steps:

  1. Create an account in CVAT.
  2. Create Organization.
  3. Switch to the Organization that you’ve created and subscribe to the Team plan.
  4. Invite members to Organization and assign User roles to invited members.
  5. Create Project.
  6. (Optional) Attach Cloud storages to the Project.
  7. Create Task or Multitask.
    At this step the CVAT platform will automatically create jobs.
  8. (Optional) Create Ground truth job.
    This step can be skipped if you’re employing a manual QA approach.
  9. (Optional) Add Instructions for annotators.
  10. (Optional) Configure Webhooks.
  11. Assign jobs to annotators by adding the annotator name to Assignee and changing the Job stage to Annotation.
  12. Annotator will see assigned jobs and annotate them.
  13. (Optional) In case you’ve created a Ground truth job give the CVAT platform some time to accumulate the data and check the accuracy of the annotation.
  14. If you are using the manual validation, assign jobs to validators by adding the validator name to Assignee and changing the Job stage to Validation.
  15. Validator will see assigned jobs and report issues.
    Note, that validators can correct issues, see Manual QA and Review
  16. Check issues and if there is a need for additional improvement, reassign jobs to either the Validator or Annotator.
  17. (Optional) Check Analytics.
  18. Export Data.

Complete Workflow Guide video tutorial

3 - Introduction to CVAT and Datumaro

We are excited to introduce the first video in our course series designed to help you annotate data faster and better using CVAT. In this introductory 4 minute video, we walk through:

  1. what problems CVAT and Datumaro solve,
  2. how they can speed up your model training process, and
  3. some resources you can use to learn more about how to use them.