Overfitting can generally be reduced by adding regularization to the model (e.g. through dropout, weight decay, a more aggressive data augmentation etc.). Also, make sure the data is sampled from the same domains from the training, validation, and test splits.
The used data augmentation also depends on the actual use case. E.g. while heavy rotations might be beneficial for images or galaxies, it might not be a good idea for landscape scenes.