In this guide, we'll be walking you through how you can use Labelbox Model to visually compare your ground truths and predictions to identify and fix label errors.
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ML models are only as good as the data they are trained on and it's actually more common than you think for there to be labeling errors in your training data. In order to improve your ML models, you'll need to improve your training data by finding label errors and then sending them to be corrected.
Once you upload your model predictions and model metrics to Labelbox, you can unlock powerful workflows to label high-impact data, faster and more efficiently. This not only helps speed up your labeling efforts and increases label quality, but can help reduce your labeling budget.
Learn more about our methods to drive data-centric iterations by improving your training data: