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Model Diagnostics

Root out all errors

Join the waitlist for the beta

Find and fix errors all in one place

Labelbox centralizes the tools you need to visualize model errors and take action to improve performance faster. Bid farewell to complex DIY solutions that combine Python notebooks, spreadsheets, and confusion matrices.

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Visualize complex patterns in your data

Use projector view to find visually similar data based on model embeddings. Select clusters of data to uncover trends in model performance and identify outliers.

See eye to eye with your data

Interact with your data visually and compare model predictions to ground truth. Get to know the nuances of your model's performance and add crucial context to metrics like confidence and IoU.

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Identify trends in model behavior

Slice and select data to surface patterns in performance. Sort and filter by heuristics like IoU and confidence, annotation classes, and custom metadata fields.

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Don't waste time with data that won't lift performance

Identify the data and classes that most often lead to model errors and prioritize labeling data that will drive more dramatic performance improvements than a random sampling.

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