Parallel Domain Raises $11 Million Series A to Accelerate Computer Vision Development
Parallel Domain, the leading synthetic data generation platform, announced its Series A funding round of $11 million led by Foundry Group, with participation from Calibrate Ventures and existing investors Costanoa Ventures, Ubiquity Ventures, and Toyota AI Ventures. The funding will be used to expand the footprint of the company’s synthetic data generation platform.
Autonomous systems, from self-driving cars to delivery drones, have the opportunity to significantly improve the quality of life for billions of people. However, training and testing these machines to perceive the surrounding world has a critical bottleneck: collecting, labeling, and curating data.
- Autonomous vehicle companies alone spend billions of dollars per year collecting and labeling data.
- Companies enlist millions of outsourced human labelers to draw and trace the annotations that the algorithms need to learn.
- Curating these datasets to include the right distribution and frequency of samples becomes exponentially more difficult as performance requirements increase.
This pattern repeats itself across nearly every application where computer vision is being used. This is neither a scalable nor reliable approach. By turning this real-world problem into a software solution, synthetic data provides a step change in accuracy, flexibility, and safety. Further, COVID-19 has underscored how vulnerable these practices are, prompting many companies to turn to synthetic data as a reliable alternative.
The Parallel Domain platform, composed of a suite of APIs and developer tools, enables customers to generate synthetic sensor data on-demand, providing essential performance improvements while reducing developer iteration time. Used by some of the world’s top AI companies, auto manufacturers, and delivery companies, Parallel Domain provides value through the full computer vision development cycle, from initial prototyping and model training to testing and post-deployment maintenance. Data that was once time-consuming, expensive, and dangerous to collect and label is now at the developer’s fingertips.
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Composed of veterans from the world’s top graphics and AI companies, such as Apple, Pixar, and Microsoft, Parallel Domain’s technology generates synthetic data at a level of realism and scale that surpasses industry alternatives. This technology is faster, produces smarter AI, and offers better economics, all without requiring developers to leave their desks.
Adrien Gaidon, Senior Manager of Machine Learning Research from Toyota Research Institute explains “Synthetic data is key to making cars and robots smarter. Thanks to its high degree of realism and flexibility, the Parallel Domain platform enables rapid exploration of cutting-edge Machine Learning ideas. Furthermore, its cost effectiveness enables accelerated paths to transfer and deployments at scale. This combination — flexibility, realism, and scalability — makes the Parallel Domain platform really unique and a huge advantage for us to develop the future of robot autonomy.”
“When our customers utilize Parallel Domain synthetic data in their training datasets, they directly improve their systems’ ability to perform critical vision tasks, such as spotting a bicyclist at night or determining the state of a traffic light,” says Kevin McNamara, CEO and Founder of Parallel Domain. “In some cases our customers see a 45% reduction in error rates while also saving the significant time they usually spend curating real-world data.”