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.
2016Vatic as a web-based annotation solution.
2017Internal version with support for images and attributes.
2018First public release on GitHub.
2020UI based on React and AntD.
app.cvat.ai as data platform.
2021Dataset as the first-class citizen.
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.
- Visit CVAT.AI
- You can ask questions anytime in public Gitter chat.
- You can find answers to your questions on Stack Overflow.
- We have is a separate Gitter chat for developers to discuss the development of CVAT.
- Visit our GitHub repository.
- Check out our Discord channel