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name: imagenetv2_classification_accuracy_eval description: Compute ImageNetV2-style top-k accuracy and drops from sampled candidate records with labels and ranked predictions.

ImageNetV2 Classification Accuracy Evaluation

Use this skill when sampled ImageNetV2-style records need top-1/top-5 metrics or an accuracy drop relative to an original validation metric.

Inputs

• Sample JSON with records containing label and ranked predictions.
• Optional original accuracy baseline.

Outputs

• top1, top5, sample count, and optional drop.

Workflow

1. Load sampled records.
2. Count whether the true label is ranked first for top-1.
3. Count whether the true label appears in the first five predictions for top-5.
4. Compute drops as original_accuracy - new_accuracy.

Validation

Run python scripts/evaluate_accuracy.py tests/fixtures/sample.json --original-top1 0.75 --output /tmp/metrics.json.

Limitations

This skill consumes precomputed predictions; it does not run a vision model.