I see what you are saying, but in that context then you lose what most people's intuitive definition of overfitting is. If I train a model on one image as my train set and then change one random pixel and run that model on this eval set then your argument would be that this is not overfitting because you are performing well on the eval set you created the model for.
My argument is that compared to models, as most people use them, micro-models are low bias and high variance, and thus overfit. That's why I set a distinction between a batman model and a batman micro-model.
My argument is that compared to models, as most people use them, micro-models are low bias and high variance, and thus overfit. That's why I set a distinction between a batman model and a batman micro-model.