Root out all errors

Find and fix errors holding you back

Visualize model errors and take action to improve performance faster. Bid farewell to complex DIY solutions that combine Python notebooks, spreadsheets, and confusion matrices.

Visualize patterns and edge cases

Quickly identify edge cases in your data using model embeddings. Cluster visually similar data to better understand trends in model performance and data distribution.

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 across different cohorts of data. 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.

Image - Hazel Erickson

Hazel Erickson
Developer, Computer Concepts Limited

Labelbox’s Model Diagnostics allows me to easily visualize our trained models and their performance, as well as recognize patterns in our training data which affects model performance. Having these diagnostic features on the same platform as where we manage our training data streamlines our processes so we can spend more time focusing on addressing model errors and building a high quality training data set to improve our model performance.