I’m not aware of any model architectures, which would be beneficial for a classification use case using an imbalanced dataset. Usually, you would use a weighted loss, add over/undersampling or manipulate the training procedure in another way to tackle the issue of an imbalanced dataset.
As usually, I would recommend to try out different architectures for your use case, as I don’t think there is a simple answer which model would work best for an unknown task.
PS: please don’t tag certain people, as this might discourage others to answer, and you might tag someone like me, who cannot give a good answer to the question.