## 摘要

We present the next generation of MobileNets based on a combination of complementary search techniques as well as a novel architecture design. MobileNetV3 is tuned to mobile phone CPUs through a combination of hardware-aware network architecture search (NAS) complemented by the NetAdapt algorithm and then subsequently improved through novel architecture advances. This paper starts the exploration of how automated search algorithms and network design can work together to harness complementary approaches improving the overall state of the art. Through this process we create two new MobileNet models for release: MobileNetV3-Large and MobileNetV3-Small which are targeted for high and low resource use cases. These models are then adapted and applied to the tasks of object detection and semantic segmentation. For the task of semantic segmentation (or any dense pixel prediction), we propose a new efficient segmentation decoder Lite Reduced Atrous Spatial Pyramid Pooling (LR-ASPP). We achieve new state of the art results for mobile classification, detection and segmentation. MobileNetV3-Large is 3.2% more accurate on ImageNet classification while reducing latency by 20% compared to MobileNetV2. MobileNetV3-Small is 6.6% more accurate compared to a MobileNetV2 model with comparable latency. MobileNetV3-Large detection is over 25% faster at roughly the same accuracy as MobileNetV2 on COCO detection. MobileNetV3-Large LR-ASPP is 34% faster than MobileNetV2 R-ASPP at similar accuracy for Cityscapes segmentation.

## 章节内容

• 回顾了MobileNet系列的进步，包括MobileNetV1、MobileNetV2、MnasNet
• 介绍了MobileNetV3人工改进的部分，包括重新设计耗时层、选择新的激活函数以及SE模块的嵌入
• 介绍了MobileNetV3-Large以及MobileNetV3-Small两个模型
• 通过实验和烧蚀研究证明MobileNetV3以及新增加模块的提升

## MobileNetV3-Large/Small

• $$SE$$表示是否在该模块中嵌入Squeeze-and-Excitation单元
• $$NL$$表示使用的非线性函数类型
• $$HS$$表示hard swish
• $$RE$$表示ReLU
• $$NBN$$表示没有批量归一化操作
• $$s$$表示步长

## 重新设计耗时层

• 对于最开始的$$3\times 3$$卷积层，使用hard swish非线性能够减少滤波器的使用，从32 -> 16
• 对于最后的反向瓶颈结构，修改如下：

## 非线性

$swish: x = x\cdot \sigma (x)$

• 改进一：Sigmoid函数在移动设备上的计算耗时，所以使用了替代版本，称改进后的swish函数为hard swish

$h-swish[x]: x\frac {ReLU6(x+3)}{6}$

• 改进二：论文发现swish函数在更多的网络层中能够发挥更好的优势（具体没说），所以在MobileNetV3中，结合使用了h-swishReLU