If you are using data augmentation the number of epochs might increase to reach the same performance but it should also allow to train even longer and thus to reduce the loss further.
Depending on the images you are using, the augmentation might also be too aggressive.
E.g. I don’t think a vertical flip performs well out of the box using “natural” images and the model would need to learn these new samples, which most likely won’t occur in the dataset.