The greedy IoU matcher gave a reference to whichever candidate was offered
first. Row order decided that, and every detection in a run shares one
transaction timestamp, so ordering by created_at left the assignment
undefined: the same QA run over the same data produced different mean IoU,
and the geometry shown to a reviewer as a false positive could be the better
of two detections. Candidates are now ranked by confidence with feature
identity as tiebreaker, which is also the COCO/PASCAL rule.
A single precision/recall/F1 triple describes one operating point, so two
models cannot be compared from it: a conservatively calibrated model looks
worse at a low confidence cut and better at a high one without detecting
anything differently. DetectionMetricsService adds the full curve, average
precision and the threshold where F1 actually peaks.
Also:
- report the population the metrics were computed over, so
matches + false_positives equals candidate_feature_count even under an
area filter; raw dataset totals move to the _raw fields;
- state whether candidates are axis-aligned boxes or footprint polygons.
A box can never reach IoU 1 against a rotated building, so the strict
score has a ceiling that has nothing to do with detection quality.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
