# Pattern Transformations and Shape Training

When you train PatMax using image training, it uses the selected space of the training image to relate the features in the image and the pattern origin you specify, to the root space of the training image.

For shape training, you must provide information that relates the shape models you supply to the root space of a hypothetical training image. You can provide this information in either of two ways.

- You can provide a training image with an associated coordinate space tree that defines the selected space of each of the shape models you are training.
- You can provide a CogTransform2D object that provides a pixel from shape model transformation.

PatMax requires the information provided using these methods so that it can compute the appropriate granularity limits for pattern training.

Note: Keep in mind that the features in the trained pattern are not the same as the shapes you provide for training. If, for example, you provided a shape that was extremely small (less than 1 pixel in size), there would be no point in training that shape as a feature, since it would not be resolved in any run-time image.