Open Source Labeler

Open Source Labeler provides a configurable, open-source labeling platform for ML data prep. It supports multiple data types (images, text, audio) and annotation modes (bounding boxes, polygons, classes). Teams import datasets, define a labeling schema, assign tasks, and perform annotations through adaptable interfaces. The core value lies in data provenance, reproducibility, and collaborative QA: every annotation is versioned, changes are logged, and review workflows enforce quality gates. Outputs export to COCO, VOC, JSONL, or custom schemas, enabling seamless integration with training pipelines. This tool benefits data scientists, annotation teams, and ML engineers seeking transparent labeling processes, auditability, and control over licensing and deployment. Core differentiators include a fully auditable history, modular interface definitions, and the ability to run self-hosted or on-premises, ensuring privacy and compliance. Use cases range from object detection datasets to sentiment labeling and transcription labeling, with support for active learning to prioritize uncertain samples for labeling. The open-source model allows researchers to customize labeling adapters, validation rules, and export plugins to fit niche domains, such as medical imaging or autonomous vehicle datasets, without vendor lock-in.