Head on over to Hacker Noon for an exploration of doing image classification at lightning speed using the relatively new MobileNet architecture. We classify images at 450 images per second!
The post covers the following:
In part 1, Creating Insanely Fast Image Classifiers with MobileNet in TensorFlow, we covered how to retrain a MobileNet on a new dataset. Specifically, we trained a classifier to detect Road or Not Road at more than 400 frames per second on a laptop.
MobileNets are made for — wait for it — mobile devices. So, let’s move our road not road model to an Android app so we can see it in action.
Let’s set some constraints so we have something specific to shoot for. We’ll attempt to:
MobileNets are a new family of convolutional neural networks that are set to blow your mind, and today we’re going to train one on a custom dataset.
There are a few things that make MobileNets awesome:
Why is this important? Many mobile deep learning tasks are actually performed in the cloud. When you want to classify an image, that image is sent to a web service, it’s classified on a remote server, and the result is sent back to your phone.
That’s changing quickly. The computational power on your phone is increasing rapidly, and the network complexity required for computer vision is shrinking (thanks to architectures like SqueezeNet and MobileNet). …