Synetic Explains Why Edge Vision Models Break—and What Complete Training Data Changes (Preview)

For the full version of this video, along with hundreds of others on various edge AI and computer vision topics, please visit https://www.edge-ai-vision.com/2026/06/why-edge-vision-models-keep-breaking-and-what-complete-training-data-changes-a-presentation-from-synetic/

David Scott, Founder and CEO at Synetic presents “Why Edge Vision Models Keep Breaking—and What Complete Training Data Changes” at the May 2026 Embedded Vision Summit.

Most edge vision deployments fail not because of model architecture, but because real-world training data is structurally incomplete. Sampled data can’t cover combinatorial edge cases, forcing perpetual retraining cycles that break embedded deployment, explainability requirements and silicon viability. In this session, Scott explores what changes when training data is complete by design rather than sampled by accident. He presents peer-reviewed results showing synthetic approaches outperforming real-world data by 34%. He explains how physics-based synthetic generation provides deterministic control over geometry, lighting, occlusion, materials and sensors, and discuss implications for deployment on processors, ASICs and FPGAs. He also introduces a new class of models that work on first deployment and explains how complete training data enables regulatory compliance.

Видео Synetic Explains Why Edge Vision Models Break—and What Complete Training Data Changes (Preview) канала Edge AI and Vision Alliance