# Images

For all machine vision applications, whether traditional or deep learning, quality, high contrast images are the key component. In Deep Learning applications, images are the primary input, and the images that are used to train the tool will determine its success. In addition, the images used to train the tool should be the same as the images that you expect to encounter during the tool’s deployment. So, the more consistent and accurately representative the images are during training, the better the tool will perform during deployment.

It's also important to remember that Deep Learning and the power of deep learning cannot overcome poor image quality. The principle of “garbage in, garbage out” applies. The quality of the input will directly influence the quality of the result that Deep Learning is able to achieve.

Note: A sequence of tools share a common database of images, but each tool has its own View and labeling information.