If my memory is right, modelling like that does poorer overall than having your general classifier find the banana.
The mental models we have to describe these things don't match well to how the ML breaks down such classifications. Finding a fruit without having already figured out that it's a banana is pretty hard.
So then you're kinda stuck with "looks like a cylinder" so your second one could distinguish hotdogs from bananas, but that's already what the layers of the neural network are doing
ML breaks down such classifications. Finding a fruit without having already figured out that it's a banana is pretty hard.
Can you elaborate? If I train a fruit detector that just gets the fruits bounding box vs a fruit detector and classifier, are you saying the latter will work better?
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If my memory is right, modelling like that does poorer overall than having your general classifier find the banana.
The mental models we have to describe these things don't match well to how the ML breaks down such classifications. Finding a fruit without having already figured out that it's a banana is pretty hard.
So then you're kinda stuck with "looks like a cylinder" so your second one could distinguish hotdogs from bananas, but that's already what the layers of the neural network are doing
Can you elaborate? If I train a fruit detector that just gets the fruits bounding box vs a fruit detector and classifier, are you saying the latter will work better?