From data volume to data value: Curating enterprise computer vision datasets through active learning and human-in-the-loop governance
Modern computer vision systems increasingly face a data-quality challenge rather than a model-capacity challenge. Large enterprise image and video datasets often contain redundant, low-quality, duplicated or inconsistently labeled samples that increase annotation cost while contributing limited incremental training value. This white paper presents a human-in-the-loop dataset curation framework designed to transform large-scale enterprise image collections into efficient, governed and reusable training datasets.
