# Image Sample Set

Once you have collected your images, you can organize them into sample sets. The image sample sets provide a helpful mechanism to manage and organize your database of images. Image sets allow you to divide your collection of images into categories that you can use 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., testing, experimentation and 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.. These sets will help to establish confidence in your results, by executing your tools against specific types of images, which you define.

For example, you could divide your database of images into categories:

- Train – Line 1 (04082019)
- Train – Line 1 (04222019)
- Train – Line 2 (03292019)
- Test – Line 1 (04092019)
- Test – Line 2 (03302019)
- Test – Bad Parts

Tip: Once you have assembled image sample sets, you can use the Display and Database Overview filters () to easily filter and sort your images.