Recall went 0.41 to 0.77 with no new labels. The bug was training on gate-approved labels only.

If you train a detector on a corpus that is only partially human-reviewed, check how you select training labels. This one cost me months. The pipeline auto-labels, then routes everything to human review. I built the trainset from labels in accepted / auto_accepted state, which felt like the careful choice. On the actual training frames, that was 7.6% of the objects present. The other 92.4% sat in review, absent from the export, and were therefore taught to the model as background. Coverage per class on those frames (labelled / actually present): sedan 193 / 33,080 0.6% rider 134 / 14,953 0.9%