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Gate filtered YOLO candidate and harden provenance
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2026-07-12 23:03:55 +02:00
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@@ -133,7 +133,9 @@ This file now starts with the current implementation status. Older preparation/b
- [x] Add deterministic visual YOLO label QA contact sheets before spending more CPU on another training run.
- [x] Filter no-data/low-variance pure-empty negative tiles from operator YOLO exports before the next training run.
- [x] Regenerate the AOI1024 cleanpx YOLO dataset with low-variance negative filtering and rerun visual contact-sheet QA before training.
- [ ] Train one inactive candidate from the filtered AOI1024 cleanpx YOLO dataset and gate it through the positive-AOI plus split-background promotion workflow.
- [x] Train one inactive candidate from the filtered AOI1024 cleanpx YOLO dataset and gate it through the positive-AOI plus split-background promotion workflow; reject it because mean positive F1 remains below gate.
- [x] Add deterministic dataset/base/trained-model SHA256 provenance to future operator training summaries.
- [ ] Review per-AOI false-negative evidence for the weakest AOIs and improve positive sample/label geometry coverage before another training candidate.
- [ ] Apply promoted V1 default building detector only after explicit operator review of the emitted `.env` updates, followed by rebuild/restart and browser/runtime smoke.
## Sprint 8 status