The data doesn't seem that imbalanced, not to cause the issues you're having. And idk what you are using for augmentation but you can def augment classes to specifically solve imbalance ( i don't like doing that personally). My next guess would be looking at how you're splitting the data for train/val and/or freezing the vast majority of the pretrained model and maybe even just training on the last layer or 2 that you add on top.

Regardless, it's something that's useful to know (very frequent in real world datasets) here's a link that goes over how to weigh classes for such cases it's with tensorflow in mind but it's the same concept regardless