@ptrblck, I don’t think you’re missing anything, it’s more likely that I am.  My only experience with PyTorch has been fine-tuning a multi-class, single-label classifier on a pre-trained ResNet model as described in the tutorial, i.e. using a single-label dataset, resetting the final layer, and using the CrossEntropyLoss function.
However, I’m not sure about all the changes I need to make to that code to produce a multi-label classification model. Obviously I need to provide a multi-label training set for fine-tuning, and change the loss function to something like MultiLabelMarginLoss, but is there anything else I need to do? I’ve searched for tutorials and code examples specifically for multi-label image classification, but haven’t found any.