Record filtered YOLO dataset evidence
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@@ -6895,12 +6895,50 @@ Open:
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- Ran `python scripts/export_operator_yolo_tile_dataset.py --help`: the CLI exposes `--drop-low-variance-negatives`, `--no-drop-low-variance-negatives` and `--blank-range-threshold` without loading GIS dependencies.
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- Ran `python scripts/export_operator_yolo_tile_dataset.py --help`: the CLI exposes `--drop-low-variance-negatives`, `--no-drop-low-variance-negatives` and `--blank-range-threshold` without loading GIS dependencies.
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- Ran `bash scripts/run_readiness_check.sh`: 460 backend tests passed, frontend typecheck passed, frontend build passed, readiness passed.
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- Ran `bash scripts/run_readiness_check.sh`: 460 backend tests passed, frontend typecheck passed, frontend build passed, readiness passed.
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## Tower runtime evidence
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- Pushed commit `a159370` and redeployed the all-in-one Tower runtime at `http://192.168.10.150:1202`.
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- Deploy validation passed:
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- live migration smoke passed;
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- browser runtime verification passed;
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- container exposed `0.0.0.0:1202->80/tcp`.
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- Regenerated the AOI1024 cleanpx dataset with low-variance negative filtering enabled:
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- dataset: `/app/storage/operator-data/yolo-building-aoi1024-cleanpx12vis035`
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- `drop_low_variance_negatives=true`
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- `blank_range_threshold=3`
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- tile count: `135`
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- positive tiles: `114`
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- negative tiles: `21`
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- skipped negative tiles: `9`
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- skipped low-variance negative tiles: `9`
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- labels: `14632`
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- train tiles: `108`
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- validation tiles: `27`
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- Dataset audit:
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- report: `/app/artifacts/operator-yolo-dataset-audit/aoi1024-cleanpx12vis035-lowvarfilter/operator_yolo_dataset_quality_audit.json`
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- status: `ok`
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- invalid labels: `0`
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- missing label files: `0`
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- median normalized box area: `0.001373291016`
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- small-box share: `0.0`
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- Visual label QA:
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- report: `/app/artifacts/operator-yolo-label-qa/aoi1024-cleanpx12vis035-lowvarfilter/operator_yolo_label_qa_summary.json`
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- contact sheet: `/app/artifacts/operator-yolo-label-qa/aoi1024-cleanpx12vis035-lowvarfilter/contact_sheet_001.png`
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- selected tiles: `32`
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- rendered tiles: `32`
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- valid labels: `7670`
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- invalid labels: `0`
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- missing images: `0`
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- missing label files: `0`
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- low-variance rendered tiles: `0`
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- Visual inspection confirmed that the previous blank white `arendonk_heide` negatives are no longer present in the review sheet. The remaining selected pure-empty negatives are real visible orthophoto/context tiles.
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## Known limitations
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## Known limitations
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- The low-variance gate is deliberately simple and only identifies visually blank/no-data-looking negative tiles.
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- The low-variance gate is deliberately simple and only identifies visually blank/no-data-looking negative tiles.
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- It is opt-in to avoid silently changing historical dataset exports.
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- Operator visual contact-sheet review remains required before any new training run.
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- Operator visual contact-sheet review remains required before any new training run.
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- The filtered dataset is now a cleaner input candidate, but model training is still not guaranteed to improve QA/QC; another training run must be gated through the existing positive-AOI and background promotion reports.
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## Next recommended pass
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## Next recommended pass
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- Redeploy Tower, regenerate the AOI1024 cleanpx dataset with `--drop-low-variance-negatives`, rerun dataset audit and contact-sheet QA, then decide whether the filtered dataset is suitable for another training run.
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- Train one inactive candidate from the filtered AOI1024 cleanpx dataset, then run the existing positive-AOI matrix and split-background promotion workflow before considering default activation.
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+2
-1
@@ -132,7 +132,8 @@ This file now starts with the current implementation status. Older preparation/b
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- [x] Add per-sample YOLO dataset audit diagnostics for parsed labels, median box area, small-box share and AOI-specific warning codes.
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- [x] Add per-sample YOLO dataset audit diagnostics for parsed labels, median box area, small-box share and AOI-specific warning codes.
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- [x] Add deterministic visual YOLO label QA contact sheets before spending more CPU on another training run.
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- [x] Add deterministic visual YOLO label QA contact sheets before spending more CPU on another training run.
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- [x] Filter no-data/low-variance pure-empty negative tiles from operator YOLO exports before the next training run.
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- [x] Filter no-data/low-variance pure-empty negative tiles from operator YOLO exports before the next training run.
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- [ ] Regenerate the AOI1024 cleanpx YOLO dataset with low-variance negative filtering and rerun visual contact-sheet QA before training.
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- [x] Regenerate the AOI1024 cleanpx YOLO dataset with low-variance negative filtering and rerun visual contact-sheet QA before training.
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- [ ] Train one inactive candidate from the filtered AOI1024 cleanpx YOLO dataset and gate it through the positive-AOI plus split-background promotion workflow.
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- [ ] Apply promoted V1 default building detector only after explicit operator review of the emitted `.env` updates, followed by rebuild/restart and browser/runtime smoke.
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- [ ] Apply promoted V1 default building detector only after explicit operator review of the emitted `.env` updates, followed by rebuild/restart and browser/runtime smoke.
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## Sprint 8 status
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## Sprint 8 status
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@@ -51,6 +51,6 @@
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- [x] Run targeted pytest for the exporter/contact-sheet tests.
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- [x] Run targeted pytest for the exporter/contact-sheet tests.
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- [x] Run `bash scripts/run_readiness_check.sh`.
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- [x] Run `bash scripts/run_readiness_check.sh`.
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- [ ] Commit and push.
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- [x] Commit and push.
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- [ ] Rebuild/deploy Tower if code changed.
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- [x] Rebuild/deploy Tower if code changed.
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- [ ] Regenerate the AOI1024 dataset with the new filter enabled, then render contact sheets and record the result.
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- [x] Regenerate the AOI1024 dataset with the new filter enabled, then render contact sheets and record the result.
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