Support larger operator training samples

2026-07-09 13:28:54 +02:00
parent a20d9b70c7
commit a1b33555b9
7 changed files with 120 additions and 11 deletions
@@ -115,9 +115,10 @@ This file now starts with the current implementation status. Older preparation/b
 - [x] Add configured-YOLO cross-tile duplicate suppression and raw/suppressed calibration evidence fields.
 - [x] Rerun live dense-AOI calibration after redeploy with `YOLO_DUPLICATE_IOU_THRESHOLD=0.5`; Westerlo 0.25 improved to F1 `0.2537313432835821` and Turnhout 0.25 improved to F1 `0.14114114114114112`, but the candidate remains rejected.
 - [x] Add `OPERATOR_YOLO_MIN_LABEL_VISIBLE_RATIO` so the next overlapping-tile dataset can drop tiny clipped edge-fragment labels.
+- [x] Add operator-only larger-AOI sample prep flags so the next training dataset is not limited to one 512x512 tile per documented sample.
 - [ ] Find or train a materially stronger aerial/Kempen building model candidate; `geointel-building-yolov8n-expanded160e50-pt` is the best current dense-AOI candidate but still too weak and too noisy for a V1 default.
 - [ ] Train a higher-capacity local aerial-building detector with stronger positive recall while preserving the hard-negative false-positive gate.
- [ ] Export and audit a visible-ratio-gated tile dataset on Tower before the next default-model training attempt.
+- [ ] Prepare `/app/storage/operator-data/operator-samples-1024` on Tower, then export and audit `yolo-building-aoi1024-visible025` before the next default-model training attempt.
 - [ ] Build the next candidate gate around better positive AOI coverage, label strategy and hard-negative retention.

 ## Sprint 8 status