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 "other", 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 "other".
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 "other").
Option 2 could work, but the numbers in the "other" 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 "larger" model would be needed in comparison to option 1. This approach would be handy if the "other" class is not similar to the other classes. For example, a model that classifies sports cars, family cars, clown cars and "other vehicles" (i.e. tractors, bikes and trucks).
Option 3 is best left for tasks that the "other" 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 "any other game" card. In this case, there are too many "other" types of game cards for you to label and model.