Labelfor

Labelfor is a data labeling platform designed to help teams create and manage labeled datasets for machine learning projects. It aims to provide end-to-end support for annotation, review, and governance, with an emphasis on reproducibility and provenance of labeling work.

The platform supports a range of annotation types, including bounding boxes, polygons, keypoints, segmentation masks, text, and audio transcripts. It offers configurable labeling schemas, workflow rules for assignment and validation, and built-in quality control features such as reviewer queues and consensus scoring. Projects can track changes over time through versioning and audit trails, enabling traceability from raw data to final labels.

Labelfor is designed for collaborative use at scale. It provides role-based access control, task distribution, and activity logging, and it can be deployed on-premises or in the cloud. The system exposes APIs and integrations with common data stores and ML pipelines, facilitating imports and exports. Data can be exported in standard formats such as JSONL, COCO, and PASCAL VOC, and can be re-imported to support iterative labeling.

The project is distributed under an open-source license, with a community-driven development model. It emphasizes interoperability with other data-management tools and ML platforms, and offers extensions or plug-ins for custom labeling schemes and connectors. In practice, Labelfor is used by research teams and enterprises to scale annotation work while maintaining quality control and auditability.