Yeah

> Just guessing here, but

is a common US English idiom that typically means, "Obviously".

You're absolutely right, though. Just by comparing the training data to the training process and serialized weights, you can see how clearly this should overfit. Once your model is noticeably bigger than a dictionary of X, Y pairs of all of your training data, it's very hard to avoid overfitting.

I volunteer with a group that develops interest and skills in science and tech for kids from historically excluded groups. I was teaching a lab on CV last month and my best student was like, "What if I train for 20 epochs, tho? What about 30?" and the performance improved (but didn't generalize as well). He didn't understand generalization yet so instead, he looked at the improvement trend and had a lightbulb moment and was like, "What if I train for 10,000 epochs???" I should check to see if his name is on the list of collaborators for the paper 😂