Scale

Enables users to label images and video frames with bounding boxes, segmentation masks, keypoints, and other computer vision annotations. Supports both 2D and 3D annotation tasks for training vision models.

Identifies and collects rare, safety-critical edge cases and corner scenarios that are underrepresented in standard datasets. Focuses on gathering data for challenging conditions like adverse weather, occlusions, and unusual object configurations.

Supports designing, testing, and iterating on annotation schemas and labeling guidelines. Allows users to refine task definitions based on pilot results and feedback.

Handles annotation of data from multiple sensor types simultaneously, including synchronized camera, LiDAR, radar, and other sensor modalities for robotics and autonomous systems.

Provides training programs and certification processes to ensure annotators understand task requirements and maintain consistent quality standards across projects.

Maintains version history of datasets, tracks changes, and documents the lineage of annotations including which annotators worked on which samples and when modifications occurred.

Combines human annotators with AI models to label data efficiently, where AI pre-labels data and humans review and correct. Reduces annotation costs while maintaining quality standards for safety-critical applications.

Manages a distributed network of human annotators to perform labeling tasks at scale. Handles worker recruitment, task distribution, quality monitoring, and payment processing.

Provides a visual, no-code interface for defining annotation tasks and schemas without requiring programming knowledge. Users can configure labeling workflows through UI components.

Measures annotation quality through inter-annotator agreement, consensus scoring, and custom quality metrics. Identifies low-quality annotations and annotators for retraining or removal.

Specialized annotation workflows tailored for autonomous vehicle perception systems, including road scene understanding, traffic participant detection, and safety-critical scenario labeling.

Enables annotation of spatial data for augmented and virtual reality applications, including 3D object placement, spatial relationships, and environment mapping.

Processes large volumes of raw data through annotation pipelines in batch mode, handling millions of samples efficiently without manual intervention for each item.

Provides clear, upfront pricing models and cost estimation tools so users can predict annotation expenses based on data volume, complexity, and quality requirements.