We will finish this chapter with a new family of CNN that not only has good accuracy, but is lighter and works faster on mobile devices.

Created by Google, MobileNet's key feature is that it uses a different "sandwich" form of convolution block. Instead of the usual (CONV, BATCH_NORM,RELU), it splits 3x3 convolutions up into a 3x3 depthwise convolution, followed by a 1x1 Pointwise CONV.​ They call this block a depthwise separable convolution.

This factorization reduces the computation and the model size: