FiftyOne Agentic Labeling
Turn natural-language prompts into annotated datasets using vision-language models. Preview outputs, refine your prompts, and scale to your full dataset — no code required.
Auto-labeling
Auto-label proprietary data without training a custom model
FiftyOne Agentic Labeling uses state-of-the-art VLMs to generate labels for both common and domain-specific concepts using natural language prompts.
Review results with built-in quality assurance workflows, correct only the predictions that need attention, and build high-quality datasets without the upfront investment of training a custom labeling model.
Author a Labeling Agent
Describe what you want labeled in natural language. Add positive and negative visual examples to sharpen the agent's understanding.
Preview generated labels
The VLM generates outputs on a handful of samples. Refine prompts until labels match your annotation guidelines.
Label the full dataset
Run the reusable agent on connected compute, returning a labeled baseline across your entire dataset.
80% faster labeling without sacrificing quality
VLMs can generalize to new categories with little or no task-specific data.
FiftyOne's VLM-based Agentic Labeling outperforms SigLIP on malaria cell classification with 95% vs. 49% accuracy across 100 samples.
Smart Data Selection
Make every annotation dollar count with FiftyOne Annotation
One in three teams (36%) say that most of their annotated data never makes it into production.
Agentic Labeling is part of FiftyOne Annotation, which helps teams identify high-value samples for labeling, ensuring labeling dollars are spent where they'll have the greatest impact.
Inside FiftyOne Annotation
Drive high-throughput, high-quality annotation
Annotation project management with configurable review workflows
Track and optimize annotator productivity with real-time performance metrics
Get started with Agentic Labeling
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