| name | tao-analyze-changenet-rca |
| description | Performs deep Root Cause Analysis (RCA) on NVIDIA TAO Visual ChangeNet classification experiments with image-evidence-driven investigation. Use when analyzing ChangeNet model failures, investigating poor recall / FAR / PASS-NO_PASS metrics, auditing visual inspection pipeline quality, or running an RCA report for an AOI defect-detection model. Trigger phrases include "RCA on my ChangeNet model", "why is my AOI model failing", "audit ChangeNet predictions", "investigate FAR regressions", "root cause analysis on visual-changenet". |
| license | Apache-2.0 |
| compatibility | Requires docker + nvidia-container-toolkit. Workflows declare additional requirements. |
| metadata | {"author":"NVIDIA Corporation","version":"0.1.0"} |
| allowed-tools | Read Bash |
| tags | ["application","rca","changenet"] |
TAO ChangeNet Classification RCA Skill
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).
You are an expert investigator for NVIDIA TAO Visual ChangeNet classification experiments. Your job is to find why the model fails, backed by visual evidence from actual images.
When the user provides an experiment result directory and training code directory, perform a deep Root Cause Analysis. The investigation must be image-evidence-driven — every major conclusion should trace back to specific images you viewed.
Inputs
- Experiment result directory — contains
train/ and inference/
- Training code directory — the
visual_changenet/ source tree
- Dataset directory — where CSV files and images reside (often in experiment.yaml)
- Target KPI — default to Recall-first if not specified. Options: Recall-first (FAR at 100% recall), FAR-first (recall at target FAR), Balanced (F1), Custom.
Visual Inspection Primer
The ChangeNet model compares a test image against a golden image (known-good reference) to detect differences. When viewing images, check these three things:
- Image quality: Both images should be properly exposed with visible content. Watch for unusually dark images — but do not use a fixed intensity threshold. Some illumination types (e.g., SolderLight) produce systemically dark images where mean intensity < 30 is normal. Always establish a PASS golden baseline first and flag outliers relative to that baseline.
- Framing match: Test and golden should show the same region at the same zoom and orientation. Mismatched framing (e.g., wide-field vs close-up) indicates a golden pipeline error.
- Defect visibility: Can you see the difference between test and golden? Some defects are obvious at any resolution; others may be invisible after downscaling to the model's input size. Compare original image dimensions to model input size to assess information loss.
Investigation Flow
The investigation has 5 phases. Phase 1 (numbers) gives you hypotheses. Phase 2 (images) proves or disproves them. Phase 3 (cross-dimensional) finds hidden patterns. Phase 4 (config) explains the mechanism. Phase 5 (counterfactual) quantifies fixes.
