Labelthe

Labelthe is a web-based data annotation platform designed to help teams prepare labeled datasets for machine learning. It supports labeling tasks across multiple modalities, including images, text, audio, and video, with annotation types such as bounding boxes, polygons, keypoints, named entities, transcription, and tagging.

Key features include role-based collaboration (annotator, reviewer, project manager), task workflows with approvals, and an audit trail for data provenance. The platform emphasizes efficiency through model-assisted labeling and active learning, offering suggestions from trained models to speed up annotation while maintaining human oversight. It provides dataset management, versioning, and modular project templates to scale labeling operations across large projects.

Interoperability and formats: Annotations can be exported to standard formats such as JSON, COCO, and Pascal VOC, and imported from common storage services. The system supports privacy controls, access management, and flexible deployment options, including on-premises installations and cloud-hosted instances.

Use cases: Labelthe is used to prepare datasets for computer vision, natural language processing, and audio processing, including tasks like object detection, image segmentation, named-entity recognition, sentiment analysis, and speech transcription.

Development and ecosystem: The project has an open development model with documentation, tutorials, and a community-contributed ecosystem of plugins and integrations with ML pipelines, data storage, and CI/CD tooling.