The input layer to the CNN that processes images, whether you are performing a regression or classification problem on those images, will need to be configured to the specific size of the images in your dataset. This can be done in the imageInputLayer - see https://uk.mathworks.com/help/deeplearning/ref/nnet.cnn.layer.imageinputlayer.html. The image inputs are typically 3-D arrays, pixel-by-pixel-by-channel (where for colour images you have 3 channels, RGB). The 4th dimension would be the observation or batch dimension, so pixel-by-pixel-by-channel-by-observation.
The division of data into training, validate and test is independent of the network task, i.e. whether it is classification or regression, and should always be considered for evaluating network performance. How much you partition your data set into these subsets is a hyperparameter that you as a network architect decide, for example, 70% training, 10% validation, 20% test.
The following example shows how to train a CNN on images for a regression task - https://uk.mathworks.com/help/deeplearning/ug/train-a-convolutional-neural-network-for-regression.html.
