Use the same workflow as was done before training. I.e. create the model, change the last layer, and load the state_dict. The most important part is to save the source code somehow to be able to get the same architecture, since the state_dict only stores the parameters and buffers without any information about e.g. the forward pass.


Use the validation data to check how much augmentation is necessary and when it hurts the model performance.


Same as above. As long as you don’t touch the test set for these experiments, you should be fine.