Applied Intuition is excited to introduce Synthetic Datasets—high-fidelity synthetic data for machine learning (ML). Synthetic Datasets empower users to improve the robustness of their ML models with camera, lidar, radar, and other sensor data. Users can generate millions of labeled samples with diverse actors, behaviors, and environmental conditions.
Obtaining data for rare events is critical to training robust ML models. However, real-world data collection is time-consuming, costly, and constrained by the frequency with which certain situations occur in the real world. Annotating the collected data is expensive and prone to errors, which causes further delays and negatively impacts model performance.
Synthetic Datasets enable perception engineers, autonomy leads, and sensor vendors to accelerate the ML development loop, train with dense labels, broaden task domains, bootstrap labels, and increase their team’s efficiency (Figure 1).
Synthetic Datasets include:
The lidar technology company Ouster already works with Applied to accelerate the deployment of their new lidar models. Ouster leverages synthetic data to mitigate overfitting and optimize ML models for new hardware.
“By working with Applied Intuition to provide high-fidelity sensor simulation to our customers, we have greatly simplified the sensor integration process and ultimately accelerated a customer’s time to autonomy,” said Mark Frichtl, CTO at Ouster.
Request a free sample dataset today, or contact our team to learn how Synthetic Datasets can facilitate your team’s ML training efforts and improve model performance.