# Bootstrap Labeling

Bootstrap labeling Labeling is the process of marking features or defects in images, or categorizing the images into classes. Labeling is crucial for training Deep Learning Lite and Deep Learning Standard tools because the labels serve as training material to guide the tools how to function correctly. is a process that can be used to reduce the time and effort required to label a large image set. Use bootstrap label to batch label views by identifying unlabeled views that the model successfully predicts. This requires that all four configuration steps have been completed at least once. These newly labeled views can then be assigned Test role, Train role, or both roles to retrain and improve the model.

Note: Bootstrap labeling is not suitable and available for all tools, for example, the Segmenter.

This involves labeling a small sample of views, training the tool, and reviewing the results. Accept the results with correct predictions to convert them into labels, and replace incorrect markings with correct labels.

To speed up labeling using the bootstrap method:

1. Label a few views.
2. Train the tool.

3. Process the view set and review the results.

4. Go back to the Labelling step.

5. Click the Show Predictions checkbox to make the predicted result visible on each view

6. Accept the predictions that are accurate by clicking Accept Prediction(s). This changes the predictions into labels.
7. For predictions that are inaccurate:

1. Click Remove Label to remove the labels and the predictions from the image.
2. Add the correct label.