Label consistency is an indicator measuring the uniformity of annotation standards in a dataset. It requires that similar targets from different annotators, different annotation batches, and different data sources have uniform category definitions, bounding box specifications, and segmentation formats. Label consistency is the basis for ensuring dataset quality and model training stability.
