Turn store footage into a stronger detection model.
One operational workspace for footage review, annotation, dataset validation, RF-DETR retraining, prediction testing, and production decisions.
Human-reviewed labels
Local data control
Evidence-driven retraining
Aisle 04 / review14:32:08
Bounding box annotation
Dataset governance
Confidence scoring
SignalObject transition detected
The complete loop
One loop across the workspace tabs.
Every stage remains inspectable. Move forward when the evidence is ready, not when a black box says so.
Core capabilities
Built for the work between model versions.
The difficult part is not starting a training command. It is preserving context, label quality, reproducibility, and operator judgment across the whole cycle.
Assisted annotation
Generate box suggestions, then keep review, correction, and final approval in human hands.
Dataset governance
Inspect exact frames, annotation state, COCO exports, class balance, and train-validation-test splits.
Training control
One worker owns the training queue while live status exposes phase, epoch, loss, and evaluation progress.
Prediction testing
Test candidate checkpoints on current footage before trusting them in the operational loop.
Monitoring readiness
Compare stable and candidate behavior before a new checkpoint becomes an operational model.
