# Creating Labels

Labels are the user-generated input data that establish the ground truth Ground truth refers to the actual nature of the problem that is the target of a machine learning model, reflected by the relevant data sets associated with the use case in question. Supervised machine learning models are trained on labeled data that are considered for the model to identify patterns that predict those labels on new data. for the tool. Ground truth refers to the undisputed facts about the images in your application, in other words, what you, as the human determine to be the key information within the image (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)., character, defect, class). Labels establish the ground truth, which is used for both 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. and performance analysis. So it is imperative that you label accurately.