Image classification - how do you deal with multi-label situations?

Hello,

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i have a problem and am looking for an intelligent solution in the following scenario:

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I have let's say 4 labels trained:

-Circle

-Square

-Triangle

-Line

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Now, i have like 1.000.000 images for each label, which should be really sufficient for solid classification.

In my software, i can set a minimal probability that the classification output.

If it's like 0.9, then a label is only accepted if p_class > 0.9.

But what happens now if there …

classification deal deeplearning