# Default Display Filters

You can find general and common selection filters in the View Browser. These filters are available in the drop-down menu of the Display field.

Filter | Description
--- | ---
all | Returns all of the Views in the database.
new | Returns newly imported images.
labeled | Returns labeled Views. Blue Locate and Blue Read tools: Views that have at least one labeled feature A feature is a visually distinguishable area in an image. Features typically represent something of interest for the application (a defect, an object, a particular component of an object).. Red Analyze tool: Views that have been labeled either good or bad. Green Classify tool: Views that have a tagged class.
not labeled | Returns Views that have not been labeled.
trained | Returns Views that were used for training Training is the process that your tool, which is a neural network, is learning about the features (pixels) based on the labels you made. For example, a tool will learn the defect/normal pixels in each image based on the defect/normal labels you drew. The goal of the tool Training is learning enough to give the correct inspection results of whether an unseen image is defective or not. The key to training is to ensure that you include all possible variations within your training set, and that your images are accurately labeled. Training times vary by the application, tool setup and the GPU in the PC being used to train the network..
not trained | Returns Views that were not used for training.

In addition, depending on the selected Deep Learning tool, there are also tool-specific filters available for each tool.

Tool | Filter | Description
--- | --- | ---
Red Analyze | good bad mismatch | Returns Views labeled as good. Returns Views labeled as bad.
Green Classify | unknown mismatch | Returns Views that contain an unknown tag and/or class. Returns Views where the predicted tag and/or class does not match the actual class
Red Analyze Standard Green Classify Standard | validated not validated | Returns only the Views that are included in the validation set The validation set is a set of images reserved for validation. The validation set is separate from the training set and test set. Since the validation set is separate, it allows you to evaluate how well the tool performs when using unseen images. The VisionPro Deep Learning application calculates a special metric, the Loss. The Loss is based on the performance of the tool on the validation set.. Returns only the Views that are not included in the validation Validation refers to a process during the training of a tool that helps to evaluate performance. Validation is like a mock exam that the tool takes during the training phase, separate from the final test. For example, validation helps you to recognize overfitting and avoid wasting time when training a tool. If you recognize that the tool is overfitting, you can stop the training early. set.
Blue Locate Blue Read | no matches | Returns Views where no Models were found (provided that a Model had been defined).