Hi @bartolsthoorn,
there seem to be few possible ways to try to do this,

  
    
    
    Equivalent of TensorFlow's Sigmoid Cross Entropy With Logits in Pytorch vision
  
  
    @AjayTalati I managed to use BCELoss, binary_crossentropy and MultiLabelSoftMarginLoss on a MultiLabel problem 
Here is the basic code 
def train(epoch):
    model.train()
    for batch_idx, (data, target) in enumerate(train_loader):
        # data, target = data.cuda(async=True), target.cuda(async=True) # On GPU
        data, target = Variable(data), Variable(target)
        optimizer.zero_grad()
        output = model(data)
        loss = F.binary_cross_entropy(output, target)
        loss.bac…
  


We really need a working example, so if you do get it working, perhaps you could post a gist?
Kind regards,
Ajay