Training our humans on the wrong dataset

I really don't want to say that I've figured out the majority of what's wrong with modern education and how to fix it, BUT When we train (fit) any given ML model for a specific problem, on which we have a training dataset, there are several ways we go about it, but all of them involve using that dataset. Say we're training a model that takes a 2d image of some glassware and turn it into a 3d rendering. We have images of 2000 glasses from different angles and in different lighting conditions and an associated 3d model. How do we go about training the model? Well, arguable, we could start small then feed the whole dataset, we could use different sizes for test/train/validation, we could use cv to determine the overall accuracy of our method or decide it would take to long... etc But I'm fairly sure that nobody will ever say: I know, let's take a dataset of 2d images of cars and their 3d rendering and train the model on that first.