It's probably overkill because the approches are different. Data augmentation allow you to use your data set and increase the volume to use it to train a model from scratch.

Transfer Learning is used mostly for generalization, you specialize a big model train on lots of data for your data (generally you have few examples and data augmentation doesn't work).

So combine both approaches can be tested but probably overkilled.