Label Studio
The most popular open-source data labeling platform for text, image, audio, and more
Leading open-source annotation platform for image, video, and 3D vision
CVAT is a leading open-source annotation platform for image, video, and 3D data, with AI-assisted labeling, QA, and analytics, available free self-hosted or via paid cloud tiers.
CVAT is the Computer Vision Annotation Tool, a widely adopted open-source platform for annotating images, video, and 3D data for computer-vision projects that require high precision and scale. Originally developed by Intel and now maintained by CVAT.ai, it has grown into one of the most widely used annotation platforms, with a large global developer community building on it. CVAT handles nearly every computer-vision annotation task: bounding boxes, polygons, polylines, keypoints, semantic segmentation, video tracking, and 3D cuboids for LiDAR and point-cloud data. It adds AI-assisted labeling, quality assurance, team collaboration, analytics, and developer APIs, making it suitable for both individual researchers and larger data-operations teams. The self-hosted version is fully free and open source under the MIT license, so teams with their own infrastructure can run it without cost, while paid cloud and enterprise tiers offer managed hosting and additional capabilities. Active development continues, with regular releases keeping the tool current for modern vision AI workflows.
CVAT is a leading open-source annotation platform for image, video, and 3D vision data, free to self-host with AI-assisted labeling, QA, and paid cloud tiers.
CVAT began as an Intel project and is now maintained by CVAT.ai. It has become one of the most widely adopted open-source annotation platforms, used by a large global community to build visual datasets for vision AI.
The company offers open-source, cloud, and enterprise products plus labeling services, spanning individual researchers to large data-operations teams. Development is active, with regular releases keeping it current.
CVAT supports the full range of computer-vision annotation tasks, from bounding boxes and polygons to keypoints, segmentation, video tracking, and 3D cuboids for LiDAR. It adds AI-assisted labeling, QA, team collaboration, analytics, and developer APIs.
The self-hosted Community edition is free under MIT, while cloud and enterprise tiers provide managed hosting and additional capabilities. Integrations and SDKs let teams fit CVAT into automated data pipelines.
CVAT targets computer-vision teams, ML data-operations groups, researchers, and organizations working on autonomous and robotics data. It is less suited to text-only NLP or audio annotation needs.
Annotators and data scientists labeling visual data.
ML/data-ops leads choosing an annotation platform.
Computer-vision engineers and researchers.
Vision AI teams needing precise, scalable image, video, and 3D annotation, with the option to self-host for free.
Verify current funding details with the vendor or public sources.
Yes. The self-hosted Community edition is fully free and open source under the MIT license, with paid cloud and enterprise tiers available.
It supports bounding boxes, polygons, polylines, keypoints, semantic segmentation, video tracking, and 3D cuboids for LiDAR and point clouds.
Yes. CVAT offers AI-assisted labeling to speed up annotation, along with QA and analytics features.
CVAT was originally developed by Intel and is now maintained by CVAT.ai, with a large open-source community.
Yes. CVAT provides developer APIs and a Python SDK, plus integrations with tools like Roboflow and cloud storage.
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The most popular open-source data labeling platform for text, image, audio, and more
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