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tao-run-deft-object-detection

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Run the full DEFT smart-data-augmentation loop for NVIDIA TAO Grounding DINO object detection: zero-shot baseline inference, KPI analysis, per-class gap analysis, SigLIP embedding of weak images, unique-neighbor mining against a source pool, ODVG dataset staging, and retraining — repeated for a fixed number of iterations. Also prepares the source pool the loop mines from, as a separate run: Co-DETR pseudo-labeling, folding to the target classes, KITTI→COCO→ODVG conversion, and embedding. Use for prompts like "run the DEFT OD loop", "run smart data augmentation for grounding dino", "mine and retrain my detection model", "improve OD mAP with gap analysis and mining", "prep the source pool", or "pseudo-label my unlabeled images for mining"; do not use for standalone TAO training, one-off inference, or gap analysis alone.

skills/tao-run-deft-object-detection/SKILL.md

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Nov 9, 2026

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Nov 6, 2026
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Apache-2.0

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# Skill: tao-run-deft-object-detection

> **Standalone install?** If this session was not initialized by the TAO skill bank plugin, run the `tao-setup` skill first (host preflight, credentials, cross-skill discovery).

## Execution Contract

Treat this as a disk-backed state machine, not as a prose recipe.

1. Preserve every explicit user value. `epoch 1` means `train.num_epochs=1`; a spec value or documented default applies only when the user did not supply that parameter. Show the source of every run parameter (`user`, `spec`, or `default`) in the Pre-Flight Summary.
2. After the user approves the Summary, initialize `deft_state.json` once with `scripts/init_deft_state.py`. Never hand-author or reinitialize it on resume.
3. Run every bundled or inline host-Python command through `scripts/deft_python.sh`. On startup, after context compaction, before every stage, and before any completion claim, run:

   ```bash
   <skill_root>/scripts/deft_python.sh \
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