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These can be thought of as unnormalized probabilities for each of Image Net's 1000 classes.

I would expect that a neural network on top of a pretrained network could give similar results, with the benefit of simpler code. Anyway, I'm looking forward to your blog post.

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.

We're thinking about releasing a blog post explaining the technical details of this project, would people be interested?

We're using Squeeze Net (https://github.com/Deep Scale/Squeeze Net), which is similar to Inception (trained on the same Image Net dataset) but is much smaller - 5MB instead of inception's 100MB - and inference is much much quicker.

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