CNN - imbalanced classes, class weights vs data augmentation

Question

I have a set of data with a few strongly imbalanced classes, eg the smallest class is about 54 times smaller than the largest. Therefore, data augmentation in order to equalize the size of classes seems to me a bad idea (in the example above each image would have to be augmented 54 times on average). So I thought that I could do less augmentation of minority classes, and then use class weights in the loss function. Is this approach better than the mere augmentation or just the use of class weights ?