Research · Vision

From vision-language model to edge detector

Use a large vision-language model to label images, then train a compact detector that runs in real time on edge hardware.

<20 msper frame on edge devices
no cloudinference on site
VLMauto-labelling cuts manual work

Pipeline

  1. Auto-label images with a vision-language model
  2. Human review of a sampled subset
  3. Train a YOLO-class detector
  4. Quantise and compile for the target device
  5. Feedback loop from operators

Figures show the typical order of magnitude for this approach compared with calling a large general-purpose model. Actual results depend on the task and data; we measure them on your data during the baseline phase.

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