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CVAT was designed to provide users with a set of convenient instruments for annotating digital images and videos.
CVAT supports supervised machine learning tasks pertaining to object detection, image classification, image segmentation
and 3D data annotation. It allows users to annotate images with multiple tools
(boxes, polygons, cuboids, circles, skeletons, etc).
Data scientists need annotated data (and lots of it) to train the deep neural networks (DNNs) at the core of AI workflows.
Obtaining annotated data or annotating data yourself is a challenging and time-consuming process.
For example, it took
about 3,100 total hours for members of Intel’s own data annotation team to annotate more than 769,000 objects for just one
of our algorithms. To help solve this challenge, CVAT.ai is conducting research to find better methods of data annotation and
deliver tools that help developers do the same.
365 Agiou Andreou, Office 201, 3035 Limassol, Cyprus
Feedback from users helps CVAT team to determine future direction for CVAT’s development. We hope to improve the tool’s user experience, feature set, stability, automation features and ability to be integrated with other services and encourage members of the community to take an active part in CVAT’s development.