tao-run-deft-aoi
Skill: tao-run-deft-aoi
When to Use This Skill
Use this skill when the user wants an agent to run the full DEFT AOI improvement loop for an NVIDIA TAO VisualChangeNet / ChangeNet PCB inspection model: baseline evaluation, RCA, synthetic defect generation, data mining, retraining, and deployment gating until a KPI target is met.
- "Run the DEFT loop"
- "Fine-tune until FAR below 0.1% at recall=100%"
- "Improve my AOI ChangeNet model using RCA and synthetic defects"
- "Iterate training until false accept rate meets the target"
Do not use this skill for a single standalone TAO training run, one-off inference, generic anomaly generation, or RCA-only analysis. Use the relevant agent directly when the user asks for only that step.
Base Model
The loop operates on NVIDIA TAO Visual ChangeNet classify with the NVIDIA C-RADIOv2-B backbone, fine-tuned end-to-end. The architecture is defined in specs/baseline_spec.yaml — that file is the source of truth. All pretrained weights come from HuggingFace (HF_TOKEN required); NGC_KEY only gates container pulls. ChangeNet backbone resolution + the staged-file/HF-URL fallback for model.backbone.pretrained_backbone_path are owned by references/visual-changenet.md. SigLIP for k-NN mining is owned by references/tao-mine-aoi-images.md. AnomalyGen-side checkpoints (Cosmos-Predict2, T5, NVDINOV2, C-RADIO-V3, DINOv2-large, SAM2, Qwen3-VL — ~22 GB for 2B-only, ~140 GB with 14B + T5-11b) live under <workspace>/augmentation/anomalygen/base_checkpoints/; the paidf-anomalygen container auto-downloads them on first use. The PCB reference dataset under <workspace>/augmentation/anomalygen/datasets/<project>/ is also auto-fetchable. See references/paidf-anomalygen.md.