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Let us suppose that I would like to train a machine learning model for classifying images according to their types (for example, photographs and drawings).

The techniques that I can use for this would be different from the techniques used for classifying images in the classes of the objects represented in them (dogs, cats, birds, etc)?

Or both tasks are so similar that I can use the same techniques in both?

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A machine learning model (simple or neural net) is agnostic of the image type or the object depicted. That is because in both cases images consist of pixels (i.e a matrix of numbers with a certain range) and a CNN model, for instance, will identify a dog or a drawing based on the image's pixels pattern (in a very high level). Therefore, both tasks can be considered similar or at least models with similar architectures can be constructed, providing correct labels are given.

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