It appears that the most common task that CNN models perform is classification. Given enough training data, CNNs excel at taking an unknown data item (e.g. an image) and assigning it to one of a set of predefined categories.
I’m looking for pointers, suggestions or guidance for moving beyond classification to a more generalized response based on image features. Specifically, I have images of gravel with a mix of particle sizes. My output is a 5 element vector of values that represent the relative proportion of particles in different size categories. My goal is to use ML to “measure” the proportions of different sizes in unknown images.
I’ve trained a fairly simple CNN (4 conv layers, 3 fully connected) to a quite high level of accuracy - if the input image more or less matches one of the particle size distributions in the training data set. However, if I test an image with a different distribution than any the network has seen during training, the results are typically poor. That is, the results are neither an accurate representation of the true particle distribution (which is what is really desired) nor the closest match from among the distributions that the network has seen during training.
It seems that what my model has learned is basically a classification with a five-element label. 
Can anyone suggest a way to proceed? With data augmentation, I currently have about 22,000 input images, in 25 different distribution groups. I am considering adding more images, from additional, different distributions, but acquiring this data will be time-consuming. Do you think it’s likely to improve the performance?
I’ve considered transfer learning. For instance, some papers have claimed that ResNet learns many texture features (which I believe is likely the main type of information needed for my task). However, ResNet was designed for classification. Can I somehow adapt it to my more general objectives?
I am a relative newcomer to machine learning, so I apologize in advance if the answer to this question is obvious.
Thanks!