Used by forefront machine learning teams to develop autonomous applications, ADAS, and smart sensors, CVEDIA’s synthetic datasets develop thorough training and classification algorithms. Use SynCity as a custom pocket laboratory to generate highly entropic scenes, conditions, and metadata. Enable real-time Hardware-In-the-Loop (HWIL), Human-In-the-Loop (HITL) or Software-In-the-Loop (SIL) simulations even with complex sensor configurations.
Validated against real data
CVEDIA creates state of the arc synthetic datasets for algorithm training. We’ve worked with some of the world’s largest organizations to distinguish how our synthetic datasets perform against field data, providing results that at times outperform real world data.
Highly entropic, high fidelity
CVEDIA synthetic datasets are optimized for algorithm training. We design each dataset to be entropic and concentrated – full of unexpected scenarios, lighting and condition edge cases, and system failure possibilities. This provides your team with a customizable, low risk means of testing safety and viability.
Reducing the gap between synthetic and real data
Synthetic data has long been a question of domain gap – if it doesn’t perform, it’s not effective, and can even be dangerous. CVEDIA has put in the effort to close that gap by optimizing synthetic data for training purposes and classifying it using real world data. Our artificial intelligence team works to create useful, training-optimized datasets by exploring the gaps that algorithms perceive between synthetic and real data, and closing them. We’re achieving significant, real world results for clients like FLIR, the world’s leading thermal sensor producer, and today we’re proving that synthetic data works.