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Getting started
- 1: CVAT Overview
- 2: CVAT Complete Workflow Guide for Organizations
- 3: Introduction to CVAT and Datumaro
1 - CVAT Overview
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:
-
CVAT Cloud: Start online with CVAT, available for free. You can also choose a subscription for unlimited data, collaboration, auto-annotations, and more.
-
Self-hosted CVAT Community Edition: Follow the Self-hosted Installation Guide for setup.
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Self-hosted CVAT Enterprise Edition: We provide Enterprise-level support for this version, including premium features like SSO, LDAP, advanced integrations with Roboflow and HuggingFace, and advanced analytics. We also offer professional training and 24-hour SLA support.
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,PPMandTIFF. - For video: all formats, supported by ffmpeg, including
MP4,AVI, andMOV.
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 | ✔️ |
Useful links
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. |
| Toloka | Cloud | Utilizes CVAT for crowdsourced data labeling and annotation services, enriching Toloka’s diverse task handling capabilities. For more information, see Integration with Toloka. |
| 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. |
| 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.
End-to-end workflow for Organizations
To use CVAT within your organization, please follow these steps:
- Create an account in CVAT.
- Create Organization.
- Switch to the Organization that you’ve created and subscribe to the Team plan.
- Invite members to Organization and assign User roles to invited members.
- Create Project.
- (Optional) Attach Cloud storages to the Project.
- Create Task or
Multitask.
At this step the CVAT platform will automatically create jobs. - (Optional) Create Ground truth job.
This step can be skipped if you’re employing a manual QA approach. - (Optional) Add Instructions for annotators.
- (Optional) Configure Webhooks.
- Assign jobs to annotators by adding the annotator name to Assignee and changing the Job stage to Annotation.
- Annotator will see assigned jobs and annotate them.
- (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.
- 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.
- Validator will see assigned jobs and report issues.
Note, that validators can correct issues, see Manual QA and Review - Check issues and if there is a need for additional improvement, reassign jobs to either the Validator or Annotator.
- (Optional) Check Analytics.
- 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:
- what problems CVAT and Datumaro solve,
- how they can speed up your model training process, and
- some resources you can use to learn more about how to use them.