FiftyOne Verified Auto Labeling
Annotation doesn’t have to slow you down — or blow your budget. Get AI into production more quickly with Verified Auto Labeling.
Automated Labeling
Get models into production faster with Verified Auto Labeling
Avoid the hidden costs associated with other auto-labeling tools.
Get auto-labeling with built-in QA
Verified Auto Labeling uses foundation models to automatically generate labels — and adds confidence scoring to prioritize the ones that require human review.
It streamlines your annotation workflow, cutting QA, and annotation costs while maintaining near-human accuracy.
Faster model iteration
Cut annotation time with label suggestions that learn from your data patterns. Bring your own models for specialized datasets.
Streamline annotation efforts
Reduce human effort while maintaining accuracy through smart automation that handles routine labeling tasks and flags edge cases for review.
Strategic data prioritization
Get end-to-end workflows that automatically identifiy and prioritize the most impactful samples for labeling to maximize model performance.
Get end-to-end workflows that automatically identifiy and prioritize the most impactful samples for labeling to maximize model performance.
Platform
Streamline your workflow with an end-to-end annotation stack
Verify auto labels with built-in QA
Work with trusted annotation partners for edge cases
Select data for annotation
Identify annotation errors
Manually correct labels
Integrate with existing annotation tools
ML Research
Auto-labeling rivals human performance
The latest paper from our ML researchers, Auto-Labeling Data for Object Detection, benchmarks auto-labeling against human annotation. We reveal how foundation models can deliver labels at near-human accuracy, while reducing annotation costs by up to 100,000×.
Annotation Calculator
Estimate your savings with our auto-labeling calculator
Data curation for annotation
Label smarter
Optimize your annotation budget by ensuring each labeled data point maximizes model performance.
Label the right data, not just more data
Use FiftyOne to identify and prioritize the most valuable data points for annotation.
Identify annotation mistakes
Surface annotation errors by highlighting samples with high false positives, unexpected predictions, detection errors, or inconsistencies in labels.
Questions?
We have answers.
Get started with Verified Auto Labeling
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