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Beyond Vision: CVAT Goes Multimodal and Introduces Audio Annotation. Learn more

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CVAT Helps Companies Build Future-Proof Vision & Audio AI Models

CVAT annotation workspace with vehicles labeled on an aerial image

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Built by Computer Vision Engineers and Open Source Contributors

  1. 2017

    CVAT began as an internal tool at Intel in 2017 to help computer vision engineers efficiently label training data. At the time, annotation was a bottleneck, and the available tools were closed, limited, or not scalable. The team built CVAT on top of VATIC, an open-source video annotation tool from Irvine, California, to automate and accelerate labeling for Intel’s internal projects.

  2. 2018

    In 2018, they later released the project as open source on GitHub, calling it CVAT (Computer Vision Annotation Tool), and it quickly grew into one of the most widely adopted annotation tools worldwide, helping both researchers and companies prepare datasets for autonomous driving, robotics, and other computer vision domains.

  3. 2022

    In 2022, CVAT spun out into an independent company, CVAT.ai. The mission remained the same: to transform raw, unstructured visual data into high-quality datasets that enable AI to solve real human problems and drive digital transformation.

  4. Now

    Today, CVAT powers annotation workflows for hundreds of thousands of teams worldwide. It has been downloaded millions of times, forked thousands of times, and contributed to by a global open-source community.

Our Mission

High-quality labeled data is the heartbeat of every AI system. Our mission is to make that foundation better, faster, and more collaborative by transforming raw and unstructured visual and audio data into high-quality datasets that enable AI to solve real human problems and drive digital transformation.

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