I don’t think there is any silver bullet for what types of augmentation will work best e.g., [1909.13719v2] RandAugment: Practical automated data augmentation with a reduced search space (arxiv.org)
However, there may still be parts of the model architecture that make it more prone to overfitting. For example, a flatten operation rather than average pooling (or some other kind of layer that removes spatial dependencies) before the fully connected layers may cause the model to be more dependent on non-generalizable features. You might want to see if the results improve with a modified ResNet or other popular vision architecture.