• Defect Detection

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Automating first pass defect detection can save hundreds of hours of manual labor. Using tools like procedural defect generation and domain randomization, we can generate a tremendous variety of synthetic data to help your vision model focus on what’s important.

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• Work Site Monitoring

 

When using AI-powered remote work site monitoring, you want your model to be capable of detecting any sort of edge case—whether that’s an unauthorized vehicle or a kid fetching their baseball. The iterative nature of synthetic training data means rapid model updates are easy, so you can always be prepared for edge cases.

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• Data Scarcity

 

In the event that you’re planning to deploy a computer vision model that detects mission critical but extremely rare failures—a split rail on a train track, for example—you may have too few organic images available to train an effective model. Synthetic data is often used to provide the variety and volume necessary to round out training data sets. (And in fact, models trained on a mix of real world and synthetic data perform better than those trained on real world data alone.)

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• Privacy Compliant

 

If you’re tracking service technician visits, foot traffic, or otherwise monitoring human activity, your models will need to be trained on human data. Rather than train your model on sourced images of real humans, which in addition to the cost can expose you to data privacy liabilities, consider using diverse and gender-balanced synthetic data. It’s both GDPR and CCPA compliant.

 

 

Save yourself time and money by taking complete control of your machine learning workflow with synthetic data.