In the second part of the Metal Camera Tutorial series we managed to convert frame data to a Metal texture. Now we are going to render it on screen with the help of a very simple Metal shader.
If anyone is wondering why would you need to use AWS for machine learning after reading this post, here’s a real example. I’ve tried training the same model with the same data on CPU of my MacBook Pro (2.5 GHz Intel Core i7) and GPU of a AWS instance (g2.2xlarge).
Although protocols are not by any means a new thing, Swift specifically encourages the developers to use it over inheritance. Not that Objective-C didn’t make use of protocols, but due to the dynamic nature of Objective-C Runtime one would be tempted to put chunks of common declarations in a superclass instead.
In the first part of Metal Camera Tutorial series we managed to fire up a session that would continuously send us frames from device’s camera via a delegate callback. Now, this is already pretty exciting, but we need to get hold of actual textures to do something useful with it — and we are going to use Metal for that.
Google’s open source TensorFlow is one of the most promising machine learning frameworks nowadays. Even though Google is said to use a slightly different version internally, and the current version of TensorFlow is somewhat behind its competitors performance wise, one can hardly argue that it has a lot of potential.