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name: imagenet_c_corruption_metrics description: Compute ImageNet-C CE, mCE, relative CE, and relative mCE from clean and corrupted top-1 error tables.

ImageNet-C Corruption Metrics

Use this skill when evaluating corruption robustness from top-1 error rates indexed by corruption type and severity. Use it for ImageNet-C or reduced experiments that preserve the same normalization equations. Do not use it for ImageNet-P perturbation stability metrics.

Inputs

• Model errors: {corruption: {severity: top1_error}}.
• Baseline errors with the same corruption/severity keys.
• Model clean top-1 error.
• Baseline clean top-1 error.
• Optional scale factor, normally 100.0.

Outputs

• ce_by_corruption.
• mce.
• relative_ce_by_corruption.
• relative_mce.
• Validation diagnostics.

Workflow

1. Validate matching corruption names and five severity levels.
2. Sum model and baseline errors across severities for each corruption.
3. Compute CE as scale * model_sum / baseline_sum.
4. Average CE values to get mCE.
5. Compute relative CE after subtracting clean error from each severity error.
6. Average relative CE values to get relative mCE.

Validation

Run:

python tests/test_corruption_metrics.py

Limitations

The script assumes error rates are already computed from predictions and labels. It does not run an image classifier.