How to Build Your Own Labelbox

Intelligently select the most valuable data samples to label for maximum model improvement.

Machine learning models automatically pre-label data to accelerate annotation workflows.

Pre-trained vision models for automatic object detection, segmentation, and classification.

Use existing ML models to generate initial predictions that annotators can review and correct.

Connect transformer-based NLP models for pre-labeling text data with custom entity recognition.

Filter and search datasets by metadata, annotations, and custom attributes with saved searches.

Export labeled data in multiple formats including COCO, Pascal VOC, YOLO, and custom formats.

Upload and process large datasets in bulk with support for multiple file formats.

Track changes to datasets and annotations over time with full audit history and rollback capabilities.

Automatically add tags, categories, and custom metadata to datasets based on annotation patterns.

Complete tracking of all user actions and data modifications for compliance and accountability.

End-to-end encryption for data in transit and at rest with support for customer-managed keys.

Fine-grained permissions for team members with project, task, and data-level security controls.

Single sign-on support for enterprise authentication systems including Okta and Azure AD.

Eye-friendly dark theme option for extended annotation sessions.

Customizable keyboard shortcuts and hotkeys to accelerate annotation workflows.

Responsive design supporting tablet and mobile devices for on-the-go annotation work.

Continue annotating without internet connectivity with automatic sync when reconnected.

Gamified performance tracking with leaderboards to motivate high-quality annotations.

Interface and documentation available in multiple languages for global teams.

Multiple annotators work simultaneously on shared projects with live updates and conflict resolution.

Tools for drawing bounding boxes, polygons, and segmentation masks on images for computer vision tasks.

Named entity recognition, sentiment analysis, and text classification tools for NLP model training.

Frame-by-frame annotation tools with tracking capabilities for object detection and action recognition in video data.

Compare multiple annotator responses and use consensus algorithms to improve label accuracy.

Compare annotation quality against industry standards and best practices for your domain.

Monitor annotation quality metrics, accuracy rates, and reviewer performance in real-time.

Real-time visibility into project completion percentage, task status, and team productivity metrics.

Automated and manual distribution of annotation tasks to team members with workload balancing.

Define conditional logic and rules to automatically route tasks and manage annotation workflows.