I have a 21-class classification problem in which there is a huge class imbalance. Since I remembered @jeremy mentioned that the better strategy for deep neural networks in this case was to oversample the minority class, what I have done is replicating examples in my csv rows via pandas before submitting it to the data loader. However, it seems that
ImageClassifierData.from_csv removes duplicate rows from my csv without asking me if I would like to, and my replicated examples go away after calling it.
Is there any way to avoid that behavior of the data loading function? Or, otherwise, to replicate minority examples with an already existing fast.ai utility?