Data Validation

Data validation is the automated, rule-based check that runs on a dataset before it ships — verifying every record conforms to the expected schema, required fields are present, label values are within the allowed set, and formatting is consistent. It catches mechanical errors (a missing field, an invalid category value, a malformed bounding box) that quality review by a human reviewer might miss because they're focused on judgment calls rather than structural correctness.

This is a distinct layer from human QA: validation is deterministic and exhaustive (it checks every record), while human review is sampled and judgment-based. A dataset can pass validation perfectly while still containing subjective quality problems, and vice versa.

Running validation as an automated gate before export — rather than discovering structural issues downstream in a training pipeline — is one of the cheapest quality investments an annotation pipeline can make.