Playing cards object detection problem
In your case you have 3 options for training your model:

Make a class for each card number, so 13 classes.
Make a class for 1, 2, 3, king, queen, jack and one bucket class for &quot;other&quot;, so 7 classes.
Make a class for 1, 2, 3, king, queen, and jack, so 6 classes. And whenever the model is not confident of any, assume it is &quot;other&quot;.

Best would be option 1.
Option 1 would be the more difficult to get data for because you need labelled data for more classes, but that should not be a problem because it seems you already have it.
Option 1 allows your model to classify all classes, and let's you achieve the same as option 2 with post processing (i.e. if predicted_class in [4,5,6,7,8,9] then &quot;other&quot;).
Option 2 could work, but the numbers in the &quot;other&quot; class are just as different from each other as to 1, 2 and 3. To be able to make this distinction, the first intuition is that a &quot;larger&quot; model would be needed in comparison to option 1. This approach would be handy if the &quot;other&quot; class is not similar to the other classes. For example, a model that classifies sports cars, family cars, clown cars and &quot;other vehicles&quot; (i.e. tractors, bikes and trucks).
Option 3 is best left for tasks that the &quot;other&quot; class is not defined, or too large to model. For example, a model that classifies if the playing cards are Uno, or Poker, or Blitz, or &quot;any other game&quot; card. In this case, there are too many &quot;other&quot; types of game cards for you to label and model.