How to do the prediction for MultiLable Classification in test time?

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Hi

I am training a multilabel classification model and my data can have multiple labels for each image, I am using BCEWithLogitsLoss for training the loss. I know BCEWithLogitsLoss has sigmoid inside so I dont apply sigmoid in the training phase.
My question is how this model will be used in the test time?
Lets say I have an image that has 3 objects in it (cat,dog,human), I will apply sigmoid to the output of the model (which has dimension of BxC where C=20=number of classes), but how I can say what classes are in the image? this is different from the single label classification and I cannot do argmax on the output. If I use the probabilities after applying sigmoid should I threshold them?! but it wont be robust to use thresholding…
Sorry if this question is very naïve

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For 0,1 labels the typical approach is usually to just use 0.5 as the threshold (e.g., just round the predictions).

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Thank you , do you know any resources that I can see how the do the evolution and how to report the performance for multi lable classification in image recognition?