Labelbox

Labelbox is a data labeling and data management platform designed to help organizations create labeled datasets for training machine learning models. The platform combines annotation tools with data governance, collaboration, and workflow automation to streamline the annotation process from data ingestion to model iteration.

Labelbox provides annotation capabilities for multiple data types, including images, videos, text, and 3D data. Supported labeling primitives include bounding boxes, polygons, polylines, points, semantic and instance segmentation masks, and text classification. Users can build custom labeling interfaces, configure review and approval workflows, and set quality controls such as consensus scoring and reviewer assignments. The system tracks dataset versioning, lineage, and metadata to support reproducibility. Labelbox also offers labeling experiments and pipelines to automate repetitive tasks and integrate human and machine labeling.

For data management, Labelbox offers cloud storage integration, data import/export, and API/SDK access for programmatic control. Exports support common formats such as COCO, VOC, TFRecord, and JSON, enabling easy ingestion into machine learning pipelines. Security and governance features include role-based access control, single sign-on, encryption at rest and in transit, audit logs, and compliant data handling for enterprise deployments.

Labelbox is used across industries such as automotive, technology, retail, and healthcare to prepare datasets for computer vision and natural language processing applications. It competes with other labeling platforms and aims to accelerate data preparation, improve labeling quality, and facilitate iterative model development through integrated collaboration and automation.