轻量级神经网络模型 Mobilenet

论文地址：MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
民间实现：caffe | Tensorflow
官方代码：tensorflow/models

有tensorflow的实现： https://github.com/tensorflow/models/blob/master/slim/nets/mobilenet_v1.md

caffe也有人实现： https://github.com/shicai/MobileNet-Caffe,

1.概述

本论文介绍了一种高效的网络架构和两个超参数，以便构建非常小的，低延迟（快速度）的模型，可以轻松匹配移动和嵌入式视觉应用的设计要求。引入的两个简单的全局超参数，使得模型可以在速度和准确度之间有效地进行折中。

2.可分离卷积

- 计算草图

- 标准卷积层的计算量

3.网络结构

//mobilenet网络结构
[net]
batch=32
subdivisions=1
height=224
width=224
channels=3
momentum=0.9
decay=0.000
max_crop=320

learning_rate=0.1
policy=poly
power=3
max_batches=1600000

#conv1 
[convolutional]
batch_normalize=1
filters=32
size=3
stride=2
pad=1
activation=relu

#conv2_1/dw
[depthwise_convolutional]
batch_normalize=1
size=3
stride=1
pad=1
activation=relu

#conv2_1/sep
[convolutional]
batch_normalize=1
filters=64
size=1
stride=1
pad=0
activation=relu


#conv2_2/dw
[depthwise_convolutional]
batch_normalize=1
size=3
stride=2
pad=1
activation=relu

#conv2_2/sep
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=0
activation=relu

#conv3_1/dw
[depthwise_convolutional]
batch_normalize=1
size=3
stride=1
pad=1
activation=relu


#conv3_1/sep
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=0
activation=relu

#conv3_2/dw
[depthwise_convolutional]
batch_normalize=1
size=3
stride=2
pad=1
activation=relu

#conv3_2/sep
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=0
activation=relu

#conv4_1/dw
[depthwise_convolutional]
batch_normalize=1
size=3
stride=1
pad=1
activation=relu

#conv4_1/sep
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=0
activation=relu

#conv4_2/dw
[depthwise_convolutional]
batch_normalize=1
size=3
stride=2
pad=1
activation=relu

#conv4_2/sep
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=0
activation=relu

#conv5_1/dw
[depthwise_convolutional]
batch_normalize=1
size=3
stride=1
pad=1
activation=relu

#conv5_1/sep
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=0
activation=relu

#conv5_2/dw
[depthwise_convolutional]
batch_normalize=1
size=3
stride=1
pad=1
activation=relu

#conv5_2/sep
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=0
activation=relu

#conv5_3/dw
[depthwise_convolutional]
batch_normalize=1
size=3
stride=1
pad=1
activation=relu

#conv5_3/sep
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=0
activation=relu

#conv5_4/dw
[depthwise_convolutional]
batch_normalize=1
size=3
stride=1
pad=1
activation=relu

#conv5_4/sep
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=0
activation=relu

#conv5_5/dw
[depthwise_convolutional]
batch_normalize=1
size=3
stride=1
pad=1
activation=relu

#conv5_5/sep
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=0
activation=relu

#conv5_6/dw
[depthwise_convolutional]
batch_normalize=1
size=3
stride=2
pad=1
activation=relu

#conv5_6/sep
[convolutional]
batch_normalize=1
filters=1024
size=1
stride=1
pad=0
activation=relu

#conv6/dw
[depthwise_convolutional]
batch_normalize=1
size=3
stride=1
pad=1
activation=relu

#conv6/sep
[convolutional]
batch_normalize=1
filters=1024
size=1
stride=1
pad=0
activation=relu

#pool6
[avgpool]

#fc7
[convolutional]
filters=1000
size=1
stride=1
pad=0
activation=leaky

[softmax]
groups=1

[cost]

4. 两个超参数

5.与其他模型精度对比

1.在ImageNet数据集上，将MobileNets和VGG与GoogleNet做对比

2.将MobileNets作为目标检测网络Faster R-CNN和SSD的基底（base network），和其他模型在COCO数据集上进行对比

6.计算量和参数量统计

Mobilenet-V1统计