MixConv: Mixed Depthwise Convolutional Kernels

Understand

Depthwise convolution is becoming increasingly popular in modern efficient ConvNets, but its kernel size is often overlooked.

• In this paper, we systematically study the impact of different kernel sizes, and observe that combining the benefits of multiple kernel sizes can lead to better accuracy and efficiency.
• Based on this observation, we propose a new mixed depthwise convolution (MixConv), which naturally mixes up multiple kernel sizes in a single convolution.
• As a simple drop-in replacement of vanilla depthwise convolution, our MixConv improves the accuracy and efficiency for existing MobileNets on both ImageNet classification and COCO object detection.