tools
Labelbox Annotate
Labelbox Annotate lets teams create, review, and manage labeled datasets for AI model training and evaluation.
Labelbox Annotate is a web-based data-labeling workspace for teams that need to annotate images, video, text, documents, audio, conversational data, and multimodal AI content. It supports built-in editors, ontology design, imported ground-truth annotations, model-assisted labeling, and collaborative review.
Teams can use internal labelers, vendors, or Labelbox labeling services. Workflow features such as batches, review stages, rework, audit history, and data-row filtering help manage quality and throughput. Labelbox also provides APIs and SDKs; model-assisted features and labeling services may involve additional usage or service costs.
Features
- 10+ built-in editors for multimodal chat, LLM evaluation, prompt/response generation, and computer vision
- Supports image, video, text, conversational text, PDF, geospatial, audio, HTML, and DICOM data
- Create ontologies with objects, classifications, and nested sub-classifications
- Import ground-truth annotations and model predictions
- Use model-assisted labeling for pre-labels, bounding boxes, and segmentation masks
- Manage labeling with batches, data rows, workflows, review, rework, and audit logs
- Export labeled data, metadata, attachments, and project details
- API reference and Python SDK available
Use cases
- Label images and video for computer-vision model training
- Evaluate and rank responses from large language models
- Create prompt-and-response datasets for supervised fine-tuning
- Annotate documents, PDFs, and text for NLP and OCR systems
- Review model predictions and send incorrect labels for rework
- Build multimodal datasets for AI agents and generative AI systems