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Record operator tile model benchmark results
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2026-07-07 21:15:58 +02:00
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@@ -101,8 +101,10 @@ This file now starts with the current implementation status. Older preparation/b
- [x] Add reproducible Geel/Mol/Turnhout operator sample preparation and multi-sample quality matrix tooling.
- [x] Run first Geel/Mol/Turnhout persisted detection quality baseline.
- [x] Add and benchmark a stronger `yolov8s` building-segmentation runtime model candidate.
- [ ] Calibrate confidence, IoU and model selection against persisted Geel/Mol/Turnhout detections and any additional local orthophoto/reference samples.
- [ ] Find or train a materially stronger aerial/Kempen building model candidate; current `yolov8s` recall remains too low for a V1 default.
- [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.
## Sprint 8 status
@@ -407,5 +409,6 @@ This file now starts with the current implementation status. Older preparation/b
- [x] Add operator-only YOLO dataset export and local training-smoke wrapper for real sample calibration.
- [x] Train/evaluate a small local GeoIntel building-detector smoke from the current operator samples and reject it because QA/QC did not improve.
- [x] Add operator-only tile-level YOLO dataset export with overlapping windows and deterministic negative tile retention.
- [ ] Run tile-level training on Tower and accept/reject the resulting local model through the persisted QA/QC matrix.
- [x] Run tile-level training on Tower and accept/reject the resulting local model through the persisted QA/QC matrix.
- [ ] Add more AOIs after the tile-level baseline so the next local model attempt is not limited to Geel/Mol/Turnhout.
- [ ] Add negative/background AOIs so the next tile dataset is not all positive tiles.