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