Open Terry-Kusunoki-Martin opened 1 year ago
I am trying to train an image classifier where image ground truth contains multiple classes. Is it possible to train a model that outputs multiple classes?
Yes, it should be not hard to adapt the code to support multiple labels. You need to modify the origial loss to either sigmoid binary cross-entropy loss or softmax based soft cross-entropy loss.
@ancientmooner appreciate the reply!
So I would be modifying this block of code here to add a BCE loss function? https://github.com/microsoft/Swin-Transformer/blob/afeb877fba1139dfbc186276983af2abb02c2196/main.py#L109
I also would need to change the output layer to match the number of classes right? Where would I make that modification?
I am trying to train an image classifier where image ground truth contains multiple classes. Is it possible to train a model that outputs multiple classes?