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name: metrics-and-evaluation description: "Routes pytorch-grad-cam explanation metrics, ROAD, ARCC, RefineCAM, and Deep Feature Factorization workflows." disable-model-invocation: true metadata: disco-role: operating license: MIT

Metrics and Evaluation

Use this sub-skill when the user wants to score, compare, or refine CAMs rather than just generate one heatmap. It covers confidence-change metrics, ROAD, ARCC, RefineCAM, and Deep Feature Factorization concept discovery.

Read first

• references/metrics-and-factorization.md for confidence-change, ROAD, and DFF workflows.
• references/refinecam-and-arcc.md for multi-layer refinement and ARCC usage.
• references/troubleshooting.md for shape, runtime, dependency, and performance issues.
• Run scripts/tiny_metric_smoke.py to verify metric wiring with a tiny synthetic model.

Typical tasks

• "How good is this CAM?" -> use ROAD or confidence-change metrics.
• "Refine the CAM across layers" -> use RefineCAM.
• "Show concept components from activations" -> use Deep Feature Factorization.
• "Compute ARCC" -> use ARCC(base_method=cam) or a matching metric helper.

Keep the target callable, input tensor, and CAM output shapes aligned. These metrics often call the model again, so use a small batch and a deterministic model when debugging.