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Distilling Vision Models 100× Smaller Than Foundation Backbones
Mission detectors do not need billion-parameter backbones. Here is how Innomium distills task-specific skills that stay accurate at a fraction of the size.
Innomium Research · June 15, 2026
Large vision foundation models are powerful teachers — but they are the wrong artifact to deploy on a forecourt NVR or browser tab.
Our approach
We distill teacher ensembles into YOLO-class students with:
- Task-specific heads for person, vehicle, and fire skills
- Hard-negative mining from operational scenes
- ONNX export paths validated on CPU and embedded targets
Results teams care about
On person detection, our distilled Sentinel student reaches 92% accuracy at under 1% of the size of general-purpose segmentation backbones — while running in real time on commodity hardware.
That efficiency is not a demo trick. It is the economics that make large-scale camera intelligence viable.