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name: imagenetv2_linear_generalization_analysis description: Fit and inspect original-vs-new ImageNetV2 accuracy relationships for rank preservation and linear generalization gaps.

ImageNetV2 Linear Generalization Analysis

Use this skill after model accuracies are available on original and new datasets. It captures the paper's observation that accuracies drop while model rankings remain close to linear rather than showing diminishing returns from adaptivity.

Inputs

• JSON list of model records with model, original_accuracy, and new_accuracy.

Outputs

• Least-squares slope and intercept.
• Mean accuracy gap.
• Pairwise rank agreement.

Workflow

1. Load model accuracy pairs.
2. Fit new_accuracy = slope * original_accuracy + intercept.
3. Compute average original-minus-new gap.
4. Compute pairwise rank agreement between original and new accuracies.

Validation

Run python scripts/linear_fit.py tests/fixtures/model_pairs.json --output /tmp/fit.json.

Limitations

This skill summarizes metric pairs; it does not explain image-level causes of the gap.