the leading synthetic data generation platform for computer vision development, has unveiled the industry’s first public synthetic data visualizer. Historically, high quality synthetic data has been inaccessible to the general public. With today’s release on Parallel Domain is letting machine learning engineers interact directly with fully-labeled synthetic camera and LiDAR datasets for developing better vision and perception models for autonomy applications.
With synthetic data, it’s not enough to just have a few great looking screenshots in a limited collection of virtual worlds. You need dynamic scenarios with complex labels and the ability to multiply those things across varied environments & conditions. It’s really great to see Parallel Domain create applications like this that allow the community to experience this caliber of synthetic data to promote better accessibility,” said Wadim Kehl, a Machine Learning Tech Lead at Woven Planet.
Parallel Domain’s website is now a gateway into its synthetic data platform. With the ability to visualize synthetic sensor data and a large menu of common computer vision data label formats, machine learning teams can now understand the uses of synthetic data before making decisions on how to best train, test, and deploy computer vision and perception models.
“ML engineers are under tremendous pressure to improve the performance of their systems and know that synthetic data should play a role. With today’s unveiling of our solution publicly, the entire ML engineering community can experience the power of using our synthetic data to train, test, and deploy ML solutions,” says Kevin McNamara, CEO and Founder of Parallel Domain.
Parallel Domain’s synthetic data visualizer is now free and available for any machine learning engineer or team member to use at www.paralleldomain.com
About Parallel Domain
Parallel Domain is the leading synthetic data generation platform. Through a suite of APIs and software tools, Parallel Domain is revolutionizing computer vision development. Develop, train, and test more accurate models while drastically reducing cost and time to deployment.