SuperAnnotate Announces Strategic Partnership With OpenCV to Improve Annotation for Computer Vision Workflows
The Partnership Will Offer Computer Vision Researchers Superior Annotation Tools
SAN FRANCISCO, June 18, 2020 (Newswire.com) - 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.
Annotated 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. 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 is an end-to-end platform for image annotation, streamlining workflows between ML engineers and annotation teams. With SuperAnnotate, annotation tasks can be completed up to 10x faster while still maintaining industry-leading levels of accuracy.
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. Released under a permissive BSD license, the library is used extensively in companies and research groups and by governmental bodies. In embedded vision systems, OpenCV is the dominant computer vision library, with 89% of the engineers using it as the primary library.
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. This will save time, money and resources for workloads across AI and machine learning.”
By leveraging the annotation capabilities from SuperAnnotate, the training process can be made vastly more efficient than with other annotation toolsets. “Coming from academia and seeing the pains of annotation first-hand, we want to make sure that every data scientist has the best possible tools and can easily connect with the right annotation teams to successfully complete their computer vision projects,” says SuperAnnotate’s CEO Tigran Petrosyan.
OpenCV.org is a non-profit organization committed to serving a large and growing AI community by building an ecosystem of AI products and services. In addition to its flagship library, OpenCV.org creates courses and hardware for AI, and supports and informs the community through its forum and newsletter. OpenCV.org is currently supported by its courses and membership program.
SuperAnnotate, was founded in 2018 by two brothers who dropped out of their machine learning PhDs from top European universities to build the fastest and smartest annotation tools. SuperAnnotate’s smart segmentation technology, developed by CTO Vahan Petrosyan while doing his PhD at KTH Royal Institute of Technology, Sweden, became the first feature in the company’s platform. Today, SuperAnnotate has become the industry’s most powerful annotation platform, bridging together best-in-class workflow automation with lightning-fast and highly accurate machine learning tools. SuperAnnotate’s investors include Point Nine Capital, Runa Capital, Fathom Capital, Berkeley SkyDeck Fund and Plug and Play Ventures. Noted advisors for SuperAnnotate include UC Berkeley AI researchers Prof. Pieter Abbeel and Prof. Trevor Darrell.
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Categories: Artificial Intelligence and Expert Systems