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Expand operator samples for YOLO hard negatives
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2026-07-07 21:42:23 +02:00
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@@ -103,8 +103,10 @@ This file now starts with the current implementation status. Older preparation/b
- [x] Add and benchmark a stronger `yolov8s` building-segmentation runtime model candidate.
- [x] Add operator-only tile-level YOLO dataset export with overlapping windows and deterministic negative tile retention.
- [x] Train and benchmark the first tile-level local YOLO candidate on Tower through the persisted QA/QC matrix.
- [ ] Calibrate confidence, IoU and model selection against additional local orthophoto/reference samples beyond Geel/Mol/Turnhout.
- [ ] Find or train a materially stronger aerial/Kempen building model candidate; `geointel-building-yolov8n-tile30-pt` is the best current overall candidate but still too weak for a V1 default.
- [x] Calibrate confidence, IoU and model selection against additional local orthophoto/reference samples beyond Geel/Mol/Turnhout.
- [x] Add negative/background AOIs to the operator sample corpus and train an expanded local tile-level YOLO candidate.
- [ ] Add a hard-negative model-quality pass with more sparse/background AOIs, balanced tile export and explicit false-positive scoring.
- [ ] 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.
## Sprint 8 status