It's easy to find good free and open source software (TensorFlow is top-of-the-line software to implement this sort of thing, and it's in our repos!) Finding large quantities of training data, on the other hand, is never free: this is the sort of data the big tech companies hord and/or invest huge ammounts of money to collect. They'll do well enough to be a pretty cool gimmick - but they won't do nearly well enough for your wife to not miss her apple version, or even to take your version very seriously. And that is what you'd never really be able to get (and that is what google collects from you every time you do one of those reCAPTCHA things).Īnd there's the issue with the pretrained models you could find out there. Certainly finding a good model is not trivial, but the oddity of many such machine learning models is that having a good model isn't nearly as important as having ridiculous ammounts of training data.
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You can install tensorflow and set this up yourself without much trouble. So apple just has a well trained model/classifier. But once the model is trained, it can be used to categorize a new image virtually instantaneously. You certainly can do it on your average home computer or laptop now, but you will not be doing it on the fly to label images. Training a machine learning model to do this well is computationally intensive: for a frame of reference, perhaps comparable to compiling a kernel. There are some freely available ones out there, but they will not be nearly as good as apple's or google's. Ah, in that case it is an image classifier.
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