# Labeling

Since the Deep Learning software is based on learning, what the network is taught about the images is vitally important. Within the Deep Learning parlance, this process is termed “labeling.” Labeling is the process of a user identifying features or defects, and graphically illustrating them on the image. The label represents the “ground truth” for the tools and is used to train the tools and validate their performance.

The label is the ground truth for the tool, i.e. you are telling the tool, "this is what it should learn." The most important part of programming the tools is ensuring that the images that are being used for training are completely and accurately labeled. Without knowing the ground truth data for the images, you cannot tell whether the tool is working properly or not. Also, without accurate labeling, the tool's training will not work as well.

Labeling is the most important part of creating a deep learning application. Remember the following:

- When you are evaluating the performance of your tools and application, performance is always measured against the labeling that you provide. If your labeling does not reflect the actual ground truth for your images, then accurate and repeatable tool performance will not mean anything.
- When you train the Deep Learning tools, the goal for training – the cost function – is attempting to train the tool to produce a response that precisely matches the labeling that you provide.