MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
具体实现代码见:https://github.com/marvis/pytorch-mobilenet
class Net(nn.Module):def __init__(self):super(Net, self).__init__()def conv_bn(inp, oup, stride):return nn.Sequential(nn.Conv2d(inp, oup, 3, stride, 1, bias=False),nn.BatchNorm2d(oup),nn.ReLU(inplace=True))def conv_dw(inp, oup, stride):return nn.Sequential(nn.Conv2d(inp, inp, 3, stride, 1, groups=inp, bias=False),nn.BatchNorm2d(inp),nn.ReLU(inplace=True),nn.Conv2d(inp, oup, 1, 1, 0, bias=False),nn.BatchNorm2d(oup),nn.ReLU(inplace=True),)self.model = nn.Sequential(conv_bn( 3, 32, 2), conv_dw( 32, 64, 1),conv_dw( 64, 128, 2),conv_dw(128, 128, 1),conv_dw(128, 256, 2),conv_dw(256, 256, 1),conv_dw(256, 512, 2),conv_dw(512, 512, 1),conv_dw(512, 512, 1),conv_dw(512, 512, 1),conv_dw(512, 512, 1),conv_dw(512, 512, 1),conv_dw(512, 1024, 2),conv_dw(1024, 1024, 1),nn.AvgPool2d(7),)self.fc = nn.Linear(1024, 1000)
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications相关推荐
- 手机CNN网络模型--MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications https://arxiv.org ...
- MobileNetV1《MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications》
MobileNetV1<MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications> ...
- 轻量化网络（一）MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
轻量化网络研究(一)MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications 研究过深度学习的 ...
- 【论文翻译】MobileNets: Efficient Convolutional Neural Networks for Mobile VisionApplications【v1, 2017.4】
文章目录 Abstract 1. Introduction 2. Prior Work 3. MobileNet Architecture 3.1. Depthwise Separable Convo ...
- 论文记录_MobileNets Efficient Convolutional Neural Networks for Mobile Vision Application
版权声明:本文为博主原创文章,未经博主允许不得转载. https://blog.csdn.net/u013082989/article/details/77970196 本文个人博客地址:点击查 ...
- Vote3Deep: Fast Object Detection in 3D Point Clouds Using Efficient Convolutional Neural Networks
下图Vote3Deep在KITTI上不可见点云的检测效果. 本文方法:在原生的3D特征(native 3D representation)上执行一个稀疏卷积,接着是一个ReLU非线性,它返回一个新的稀 ...
- Bag of Tricks for Image Classification with Convolutional Neural Networks（卷积神经网络在图像分类中的技巧）
来源:Tong He Zhi Zhang Hang Zhang Zhongyue Zhang Junyuan Xie Mu L Amazon Web Services fhtong,zhiz,hzaw ...
- 【读点论文】CMT: Convolutional Neural Networks Meet Vision Transformers
CMT: Convolutional Neural Networks Meet Vision Transformers Abstract 视觉transformer已经成功地应用于图像识别任务,因为它 ...
- One-Pass Multi-task Convolutional Neural Networks for Efficient Brain Tumor Segmentation
method: 首先:用三个网络来训练,分别针对comlete区域,core区域,和enhancing区域(使用网络OM-net) 1.使用随机采样从MRI大脑图像中采块,训练,分类器分为5类,最后测 ...
最新文章
- python小课骗局-谈谈学风变python小课感想，菜鸟表示真的挺简单
- python安装路径怎么找-Python---查看安装路径
- OpenCV Shi-Tomasi角点检测
- jmeter图片的下载
- JAVA成员变量为什么不能在类体中先定义后赋值
- weblogic数据源配置的问题,weblogic密码破解
- java 构造 statict_java学习：构造方法、static、final
- 目录下文件过多无法删除
- 项目交换成功——PM（李忠）
- 80坐标系(3度带)转经纬度
- chapter 1 JS简介，核心语法，变量
- NXP S32K146 CAN通讯 TJA1043（一）
- 域名劫持的特征都有哪些？
- 不忘初心牢记使命文化墙励志标语墙贴
- 腾讯地图JS API制作专题图
- 常见的电脑系统故障原因及解决办法
- C/C++基础进阶篇：C++11 中引入的 delete 描述符使用场景
- SpringBoot导入导出你会用吗？（EasyPoi）
- AAC音频文件添加ADTS头
- QT学习之路-资料收藏集锦