Train a high-performance ML model with synthetic data to reduce real data collection and labeling.
Utilize synthetic images to address edge cases, target data sparsity issues, and expand coverage to new countries and regions.
Immediately obtain new training datasets when failures occur in testing or production, rather than waiting for collection and labeling.
Physically accurate synthetic data generated with validated, hardware-specific sensor models.
Error-free ground truth labels in industry-standard formats.
Proprietary, state-of-the-art techniques to reduce and mitigate the simulation-to-real domain gap.
Thousands of sign faces and posts with procedurally generated variants.
Different environments and weather to maximize diversity and ensure model robustness.
Regions include the U.S., Canada, Europe, Japan, China, South Korea, and more.