labelswhere

Labelswhere is a conceptual framework for organizing, querying, and standardizing labels used in data annotation across machine learning projects. It focuses on metadata about labels rather than the data items themselves, enabling consistent labeling practices and easier analysis of label usage.

Core concepts in labelswhere include labels, label sets, categories, attributes, and provenance. A label represents a discrete annotation, a label set groups related labels, and attributes describe properties such as intensity or confidence. Provenance tracks who annotated, when, and under what workflow rules. Versioning supports changes over time, allowing teams to audit and revert label schemas as projects evolve.

In practice, labelswhere is implemented as a metadata layer atop storage of labeled data. It supports project management, role-based access, and customizable labeling workflows with tasks, reviews, and quality checks. An API and user interface enable creation and editing of taxonomies, assignment of labels to items, and export of results. The approach emphasizes interoperability with common annotation formats and tools through import/export adapters.

Common use cases include harmonizing labeling across teams, managing taxonomies for large corpora, and enabling cross-project analysis of label distributions. Export formats often align with standard schemas such as COCO-style JSON or VOC, and the framework can generate analytics on label frequency, co-occurrence, and inter-annotator agreement.

Adoption considerations include governance of taxonomies, privacy and access control, and licensing of tools. While several annotation platforms offer similar metadata-management features, labelswhere serves as a conceptual approach to label management rather than a single canonical product.