Hello. I am using fast to perform image classification on Pap smear Herlev dataset. This dataset is organised into Imagenet style dataset. Moreover, I require to print out two accuracy metrics: One for binary classification and other for multi class classification.
If you just want to be quick, train for seven classes and then calculate accuracy for aggregated classes too. However, I would also train for binary classification, because you may have better binary accuracy.
So you are already training twice.
No need for multi-labels. I would compare accuracy of the first model vs accuracy calculated after training the second one, by aggregating 3 classes as normal and the other 4 as abnormal, and keep the best.