Add operator YOLO dataset quality audit

2026-07-09 01:45:52 +02:00
parent 65808ac994
commit 5898e548c2
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@@ -5643,3 +5643,24 @@ Tested:

 Open:
 - The expanded160e50 model is consistently best on the current positive AOI portfolio, but hard-negative/background AOI evidence still prevents blind default promotion.
+# Sprint 146 - Operator YOLO dataset quality audit
+
+## What changed
+
+- Added `scripts/audit_operator_yolo_dataset_quality.py` to inspect generated operator YOLO tile datasets before further model training.
+- Added a focused pytest that creates a synthetic tile summary and YOLO label files, then verifies JSON/Markdown audit output and warning gates.
+- Added the audit script to the readiness syntax gate.
+- Documented the operator audit command in `scripts/README.md`.
+
+## What was tested
+
+- `python -m pytest backend\tests\test_sprint146_operator_yolo_dataset_quality_audit.py -q`
+
+## Known limitations
+
+- The audit is evidence tooling only. It does not modify datasets, train models, fetch external data or change active YOLO configuration.
+- The report flags likely dataset risks, but final promotion decisions must still come from persisted detection QA/QC matrices and hard-negative benchmarks.
+
+## Next recommended pass
+
+- Run the audit against the existing Tower tile datasets and use the results to decide the next training-data expansion pass.