# Perturbation

The ViDi neural network can only be trained to learn the features in images that it actually sees. In an ideal world, your training image set would include a representative set of images that expressed all of the normal image and part variations. In most cases, however, training needs to be performed with an unrepresentative image set. In particular, image sets are often collected over a short period of time, so normal part and lighting variations over time, as well as changes or adjustments to the optical or extrinsic characteristics of the camera are not reflected.

The ViDi training system allows you to augment the image set by specifying the types of appearance variation that you expect during operation, through the use of the Perturbation parameters, such as the following:

- Luminance
- Contrast
- Rotation
- Scale

For each perturbation type, you can specify the range for the perturbation.

Note: Perturbation is not a substitute for collecting and training actual images. In particular, the image perturbations can do no more than approximate the actual changes in the appearance of real parts or scenes.