Annotate smarter with CVAT, the industry-leading data annotation platform for machine learning. Used and trusted by teams at any scale, for data of any scale.

Computer Vision Annotation Tool (CVAT) is a free, open source, web-based image and video annotation tool which is used for labeling data for computer vision algorithms. CVAT supports the primary tasks of supervised machine learning: object …

CVAT is the premier open-source image and video annotation tool, empowering AI development with quality data. Thousands of teams use CVAT worldwide every day.

Before you can use CVAT, you’ll need to get it installed. The document below contains instructions for the most popular operating systems. If your system is not covered by the document it should be relatively straightforward to adapt the instructions below for other systems.

Start using CVAT online: cvat.ai. You can use it for free, or subscribe to get unlimited data, organizations, autoannotations, and Roboflow and HuggingFace integration. Or set CVAT up as a self-hosted solution: Self-hosted Installation Guide.

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.

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.

Aug 20, 2024 · In this CVAT annotation tool review, we take a look at its major features, pros, and cons. Learn about implementation and ML use cases of CVAT labeling.

Apr 26, 2024 · Explore the capabilities of CVAT in AI and machine learning - a versatile, user-friendly tool for advanced computer vision annotation.

Mar 4, 2024 · With CVAT, users can efficiently annotate their datasets by drawing 🔗 bounding boxes, 🔗 polygons, 🔗 lines and points on images, or tagging time intervals on videos. CVAT also supports a wide range of annotation formats, making it flexible for different Computer Vision tasks and compatible with various machine learning frameworks. ‍