Labelbox makes it simple to build workflows that import pre-labeled data for labeling teams to review and adjust directly. The result for many customers has been savings of 50-70% in terms of annotation costs.
Automate labeling where your model confidence is high, and spotlight assets where model performance is low.
Guide labelers to pre-labeled assets so they can confirm, reject, or edit annotations rather than start labeling from scratch.
Dramatically improve model performance by focusing labeler time on more examples of data with low confidence predictions.
Speed up labeling by drawing attention to the most relevant areas to label so labelers don’t need to analyze the entire asset.
Model-assisted labeling is just one feature of an entire labeling system purpose-built to help you iterate on labeling projects more quickly and bring your AI into production faster.
Model-assisted labeling is easy to set up using the Python SDK. Learn more about how to get started.