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Record operator YOLO dataset audit results
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2026-07-09 01:47:08 +02:00
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docs/CODEX_EXECUTION_LOG.md
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## What was tested
- `python -m pytest backend\tests\test_sprint146_operator_yolo_dataset_quality_audit.py -q`
- `bash scripts/run_readiness_check.sh`
- Tower live audit after pulling commit `5898e54`:
- `yolo-building-tile-dataset`: `needs_attention`; only 3 positive samples and no background negatives.
- `yolo-building-tile-expanded160`: `ok`; 10 samples, 8 positive samples, 360 tiles, 11,213 labels, no missing/invalid label rows.
- `yolo-building-tile-hardneg160r4`: `needs_attention`; repeated background negatives are 91.1% of negative tiles.
- `yolo-building-tile-hardneg160r8`: `needs_attention`; repeated background negatives are 95.4% of negative tiles.
## Known limitations
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## Next recommended pass
- Run the audit against the existing Tower tile datasets and use the results to decide the next training-data expansion pass.
- Add more unique hard-negative/background AOIs before another hard-negative training run. The current label files are clean, so the bottleneck is dataset diversity and balance rather than label-file corruption.