Is there a complete code example based on Pytorch about multiple labels classification with unbalanced dataset?
Do you mean “multi-label classification”, where each sample might belong to zero, one, or multiple classes or “multi-class classification”, where each sample belongs to a specific class only?
We have a few code snippets in this forum showing how to use a weighted loss or weighted sampling to counter the effect of an imbalanced dataset.
I mean multi-label classification, i.e., multiple classes can be assigned to each sample. The code snippets you mentioned i have seen. Actually , i am learning Pytorch framework, i just need a more complete code , that is, given one example dataset , the code has shown how to train the classifier and output the accuracy.