tao-run-deft-od-aoi

TAO DEFT OD AOI

This application is a disk-backed RT-DETR loop for one foreground class, defect. Its core is real-data-only; AnomalyGenNext synthesis is an optional route with separate preparation, generation, and admission gates.

References

Read only the references needed for the current stage:

• intake and launch: references/defaults.md, references/data-contract.md, references/source-manifest.md, and references/preflight.md;
• orchestration: references/pipeline.md and references/scripts-and-agents.md;
• gaps and retrieval: references/gap-routing.md and references/tao-analyze-gaps-od-map.md;
• training and selection: references/training-policy.md;
• optional synthesis: references/anomalygen-pool.md.

Start

Select an installed platform, read its skill, then invoke tao-launch-workflow. The single launch review must include the four normalized COCO roles, trainable RT-DETR base checkpoint, maximum iterations, image and Data Services containers, GPU shape, and expected runtime. After approval, copy assets/default_policy.yaml, fill its required values, and initialize once:

scripts/init_deft_od_aoi.py \
  --config /workspace/deft_policy.yaml \
  --output-dir /new/results/deft_contract

Never reinitialize an existing result. The validator requires disjoint KPI, test, defective-real, and verified-clean roles; every COCO must declare only defect. KPI and test may mix boxed and boxless images because they never enter training. Every defective-real image needs at least one box, while clean images remain explicit zero-annotation COCO entries.

Loop boundary

The loop composes existing bank actions:

1. tao-train-rtdetr inference on KPI and test.
2. Two tao-analyze-gaps-od-map actions: loose confidence for FP routing and strict confidence for FN routing.
3. tao-generate-image-embeddings with one frozen SigLIP encoder, followed by tao-mine-od-images unique-neighbor matching against the real or clean role.
4. Application-owned admission and cumulative binary COCO assembly.
5. Direct tao-train-rtdetr training from the same frozen base checkpoint, then KPI-only checkpoint selection. Test remains report-only.