# Test Set

Similar to the 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. set, you will want to have your test set contain representative variations of what you expect to encounter during runtime. The key differentiation between the training set and the test set is that the test set are not included during the training of the tool. Instead, the test set are used to evaluate the results of training and thus they are also called as evaluation set. The remainder of the images that are excluded from the training set will automatically be the test set.

You will also want to label the test set in the same way that you label the training set. This will allow you to perform statistical analysis of training set, to determine how well the tool is generalizing its results.