Record operator tile model benchmark results

2026-07-07 21:15:58 +02:00
parent 1d27e4a059
commit e8d79fccbb
3 changed files with 37 additions and 5 deletions
@@ -13,6 +13,11 @@
 - Added readiness coverage for the tile exporter Python compile check.
 - Added regression coverage in `backend/tests/test_sprint130_operator_yolo_tile_dataset.py` for script contract, help behavior without GIS imports, edge-covering tile windows and deterministic negative-tile selection.
 - Updated operator documentation for tile-level dataset export and reuse of the existing local training wrapper.
+- Live Tower tile export produced `/app/storage/operator-data/yolo-building-tile-dataset` with 75 overlapping tiles and 5321 clipped building labels from the Geel/Mol/Turnhout operator samples.
+- Live Tower 30-epoch CPU training produced `/app/models/geointel-building-yolov8n-tile30.pt`; the model catalog exposes it as `geointel-building-yolov8n-tile30-pt` with SHA256 `b9e228202500d7c85836d12a72e320f4f2f0cef24cbb1b5bf7fa78a6778390af`.
+- Live YOLO preflight loaded `geointel-building-yolov8n-tile30-pt` successfully with `status=ready`, `model_load_ok=true`, `manifest_valid=true`, `tile_paths_exist=true`, `will_download_models=false` and `will_run_inference=false`.
+- Live 48-run Geel/Mol/Turnhout QA matrix compared `geointel-building-yolov8n-tile30-pt` with `yolov8s-building-segmentation-pt`; best overall score was Mol with the tile model, tile `640`, threshold `0.15`, precision `0.13602941176470587`, recall `0.09893048128342247`, F1 `0.11455108359133127`.
+- Result decision: the tile-trained local model is now the best tested candidate on Geel/Mol and best overall, but remains experimental and should not become the V1 default until more AOIs and negative/background samples materially improve recall and false-positive behavior.
 - No Training Studio UI, API contract change, provider fetching, model auto-provisioning or app-side model training behavior was introduced.

 ## Sprint 129 Operator YOLO training dataset tooling (2026-07-07)