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Interesting!

I'm curious why you've used a different classification algorithm on top of a neural network. I would expect that a neural network on top of a pretrained network could give similar results, with the benefit of simpler code. Is performance the reason?

Anyway, I'm looking forward to your blog post.

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Training a neural network on top would require a "proper" training phase, and finding the right hyperparameters that work everywhere turned out to be tricky. Actually, this is what we did originally, in the blog post we'll try to show demos of each of the approaches and explain why they don't work.

KNN also makes training "instant", and the code much much simpler.

That makes sense.

By the way, I think your software could become very popular on the Raspberry Pi, because it would be very cheap and fun to use it for all sorts of applications (e.g. home automation).

https://github.com/PAIR-code/deeplearnjs/issues/158

Basically, read this paper: https://www.cs.cmu.edu/~rsalakhu/papers/oneshot1.pdf

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