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 fall within the allowed set, and formatting is consistent. It catches mechanical errors, such as a missing field, an invalid category value, or a malformed bounding box, that quality review by a human reviewer might miss because they are focused on judgment calls rather than structural correctness.

This is a distinct layer from human QA: validation is deterministic and exhaustive, since 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 the reverse can also be true.

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. A malformed record caught before it leaves the pipeline costs almost nothing to fix compared to one discovered after a model has already trained on it.