How to choose layer from which to unfreeze image classification model
Regarding #1: As you mentioned, the following amount of fitting you do on a pre-trained (PT) network depends on the relative size and similarity of your data and the data used to train the PT model.


If our dataset is smaller than the PT data, we should unfreeze fewer layers to avoid overfitting.
If our dataset is of a similar size as the PT data, we can unfreeze more layers since overfitting is less of a concern.


Essentially, more frozen layers means less overfitting, since those layers will retain the relatively generic features of the PT network. As such, you can use hyperparameter tuning (and the results of your cross-validation) to optimize this like you would any other hyperparameter (grid search, etc).

Regarding #2: Yes, you certainly can use the PT model to fit and make predictions. But, the more you understand the model's architecture, the better you will be able to tune the model, and ultimately, improve your results.