I have a use case where the model needs to detect fabricdefects. There are 15+ different kinds of defects. In one image there can be multiple defects present. The straight forward solution for this should be a multilabel model from my understanding. The classification of the data for a multilabel model is extremely tedious and errorprone.
Now I use a multiclass model, which seems to produce ok results. A multiclass model has only one right output class. My goal is to add one image to multiple classes and if the model predicts one of these classes it should result in a correct prediction (or lower loss output).
For example if an image contains defect1 and defect2 the model should look at both these outputs and calculate the loss from the one that has the highest probabilty.
Now my question: "Is it possible to have a model where the samples can have multiple right output classes, but are not penalized by predicting only one right class?"