Easily define and generate synthetic datasets at scale by using natural language scenario generation, Synthetic Datasets’ distribution-based domain randomization framework, or by extracting and augmenting scenes from real-world logs. Directly control distributions to ensure datasets match the task domain and target specific edge cases while being designed to have a minimal domain gap during training.

Synthetic Datasets build upon the capabilities of Applied Intuition’s Sensor Sim to ensure synthetic data is physically accurate and representative of target sensors and task domains. Since machines look at data differently from humans, Synthetic Datasets have the diversity and realism necessary for machines to get value from training on the data.

Programmatically generate ground truth labels ranging from simple bounding boxes and cuboids to dense labels like optical flow and depth. Customize labels to your taxonomy, ontology, and labeling specification to ensure data seamlessly integrates with existing datasets and ML pipelines.

Use domain adaptation based on real training datasets. Re-style or modify synthetic data to match the task domain through a combination of generative and classical algorithms, ensuring that synthetic datasets provide maximal value to ML-enabled systems.

Utilize Applied Intuition’s Cloud Engine to orchestrate thousands of parallel simulations and generate production-scale datasets in a matter of hours.