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name: imagenetv2_candidate_pool_schema description: Validate and normalize ImageNetV2-style candidate pools with class labels, MTurk selection frequencies, and ranked model predictions.

ImageNetV2 Candidate Pool Schema

Use this skill when a recovery or dataset-building task needs an auditable ImageNetV2-style candidate pool. Do not use it to sample final datasets or score model accuracy; those are downstream skills.

Inputs

• JSON list of candidate records.
• Each record must include candidate_id, class_id, label, selection_frequency, and predictions.

Outputs

• Normalized records sorted by class_id and candidate_id.
• Validation errors for missing fields, invalid frequencies, or malformed predictions.

Workflow

1. Load candidate records from JSON.
2. Validate required fields and types.
3. Enforce selection_frequency in [0, 1].
4. Normalize labels and prediction ids to strings.
5. Save normalized records for sampling and evaluation.

Validation

Run python scripts/candidate_pool.py tests/fixtures/candidates.json --output /tmp/normalized_candidates.json.

Limitations

This skill validates metadata and predictions only; it does not verify real image pixels or run MTurk.