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Gary Bradski, President of OpenCV, said, “We are very excited that SuperAnnotate is willing to provide these capabilities for OpenCV and believe that it will greatly benefit the community with more robust annotation tools. In embedded vision systems, OpenCV is the dominant computer vision library, with 89% of the engineers using it as the primary library. Released under a permissive BSD license, the library is used extensively in companies and research groups and by governmental bodies. OpenCV library is the largest collection of open source computer vision algorithms (2,500+ optimized algorithms) in the world. The library boasts of more than one million downloads a week and is used by millions of developers around the world. This highly optimized library is developed using C++ with binding for languages like Python and Java. With SuperAnnotate, annotation tasks can be completed up to 10x faster while still maintaining industry-leading levels of accuracy.

SuperAnnotate is an end-to-end platform for image annotation, streamlining workflows between ML engineers and annotation teams. Data preparation and engineering tasks represent over 80% of the time consumed in most AI and machine learning projects. With this new partnership, both organizations are focused on addressing the community’s need for efficient and accurate annotation. ​​​​​​​​SuperAnnotate, an AI-powered annotation platform for machine learning engineers and annotation teams, and OpenCV, the leading open source computer vision platform, announce a strategic partnership to significantly improve the annotation process and toolset around computer vision. As part of the new partnership, SuperAnnotate will join OpenCV’s membership program and will make its annotation canvas available for data scientists to use in their projects, further integrating the canvas as an available component of the OpenCV project.Īnnotated data is paramount for advances in self-driving cars, retail, healthcare and other industries the market for data annotation tools is expected to reach $2.57 billion by 2027.