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Document stronger building model benchmark
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2026-07-07 05:03:44 +02:00
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CHANGELOG.md
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# Changelog
## Sprint 128 Stronger building model runtime benchmark (2026-07-07)
- Added `keremberke/yolov8s-building-segmentation` as an explicit Tower runtime model asset at `/mnt/user/appdata/geointel/models/yolov8s-building-segmentation.pt`; the file is not committed to Git.
- Verified the live model catalog exposes `yolov8s-building-segmentation-pt` with `will_download_models=false` and SHA256 `a27af31654c6a4edbdc85581c33d93c13986b5919de7de410f8d85d801b3bb34`.
- YOLO preflight loaded the model locally with `model_load_ok=true` and no automatic download.
- Ran a 36-run Geel/Mol/Turnhout matrix comparing `yolov8n-building-segmentation-pt` and `yolov8s-building-segmentation-pt` across tile sizes `512`/`640` and thresholds `0.50`/`0.25`/`0.15`.
- Best overall score was Mol with `yolov8s-building-segmentation-pt`, tile `640`, threshold `0.15`: 55 detections, 9 matches, 46 false positives, 365 false negatives, precision `0.16363636363636364`, recall `0.02406417112299465`, F1 `0.04195804195804196`.
- Conclusion: `yolov8s` is cleaner than `yolov8n` on some samples, but still misses most GRB buildings; it is not a sufficient V1 default.
## Sprint 127 Multi-sample detection quality calibration tooling (2026-07-07)
- Added `scripts/prepare_operator_real_data_samples.py` to prepare documented Geel, Mol and Turnhout orthophoto/GRB GBG building sample pairs as explicit runtime artifacts.