Meet MixNet: Google Brain’s new State of the Art Mobile AI architecture.

Summary: By replacing single convolutional kernels with a mixed grouping of 3x3–9x9 kernels, and a neural search ‘MixNet’ architecture, a new state of the art 78.9% accuracy on ImageNet top 1% is achieved under standard mobile metrics. MixNet-L outperforms ResNet-153 with 8x fewer params, and MixNet-M matches it exactly but with 12x fewer params and 31x fewer FLOPS.

Tan and Le of Google Brain recently show-cased a new depthwise convolutional kernel arrangement (MixConv) and a new NN architecture optimized for efficiency and accuracy using MixConvs in their paper: Mixed depthwise convolutional kernels.

This article will summarize the architecture of MixConv’s, the core building block for MixNet, and the MixNet NN architecture itself in preparation for you to use in your own deep learning projects. (Personally I’m planning to use on our next run at the FastAI leaderboards).

Let’s start with results: MixNet was able to surpass the current suite of mobile architectures as shown below and set a new top 1% record (State of the Art) with MixNet-L:

MixNet versus current suite of mobile deep learning architectures. (from paper).

Perhaps even more impressive are the computational efficiency comparisons. Mobile based AI naturally requires maximum computational efficiency and comparing MixNet-M and MixNet-L to a ResNet-153 is ……