MixConv: Mixed Depthwise Convolutional Kernels

TL;DR

This paper introduces MixConv, a novel mixed depthwise convolution technique that combines multiple kernel sizes to improve the accuracy and efficiency of mobile neural networks like MobileNets and MixNets, achieving state-of-the-art results.

Contribution

The paper proposes MixConv, a new convolution method that mixes multiple kernel sizes within a single layer, enhancing model performance and efficiency over existing mobile network architectures.

Findings

MixConv improves accuracy and efficiency of MobileNets.

MixNets outperform previous mobile models on ImageNet.

MixNet-L achieves 78.9% top-1 accuracy on ImageNet.

Abstract

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. To demonstrate the effectiveness of MixConv, we integrate it into AutoML search space and develop a new family of models, named as MixNets, which outperform previous mobile models including MobileNetV2 [20] (ImageNet top-1 accuracy +4.2%), ShuffleNetV2 [16] (+3.5%), MnasNet [26] (+1.3%), ProxylessNAS [2] (+2.2%), and FBNet [27] (+2.0%). In particular, our MixNet-L achieves a new state-of-the-art 78.9% ImageNet top-1 accuracy under typical mobile settings (<600M FLOPS). Code is at https://github.com/ tensorflow/tpu/tree/master/models/official/mnasnet/mixnet