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Record AOI1024 clean YOLO candidate gate
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2026-07-09 17:22:33 +02:00
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@@ -116,10 +116,12 @@ This file now starts with the current implementation status. Older preparation/b
- [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.
- [x] Regenerate Tower AOI1024 operator samples with paged GRB references and verify no dense reference sample is capped at 1000 features.
- [x] Export and audit AOI1024 clean-label variants; select `yolo-building-aoi1024-visible050-minpx8` as the first audit-passing 512px training candidate.
- [x] Train and gate `geointel-building-yolov8s-aoi1024clean512e50-pt` through seven positive AOIs and nine hard-negative/background AOIs.
- [ ] 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.
- [ ] 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.
- [ ] Train a background-aware local aerial-building detector with stronger positive recall while reducing hard-negative false-positive pressure.
- [ ] Build the next candidate gate around cleaner positive labels plus stronger unique hard-negative retention.
## Sprint 8 status