LightWeightCNN

轻量化卷积神经网络实现

CNN Architecture	Data Type (bit)	Model Size (MB)	GFlops （1080Ti）	Top-1 Acc(VOC 07+12)	Top-5 Acc(VOC 07+12)
AlexNet	32	233.081	1.429	68.24%	94.22%
SqueezeNet	32	4.793	1.692	75.46%	96.78%
SqueezeNetBypass	32	4.793	1.692	77.54%	97.41%

内容列表

• LightWeightCNN
  • 内容列表
  • 背景
  • 安装
    • 文档工具安装
    • Python库依赖
  • 用法
    • 文档浏览
  • 主要维护人员
  • 致谢
    • 引用
  • 参与贡献方式
  • 许可证

背景

• SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size
• MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
• MobileNetV2: Inverted Residuals and Linear Bottlenecks
• ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile
• ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design

安装

文档工具安装

$ pip install -r requirements.txt

Python库依赖

$ cd py
$ pip install -r requirements.txt

用法

文档浏览

有两种使用方式

1. 在线浏览文档：LightWeightCNN

2. 本地浏览文档，实现如下：

   $ git clone https://github.com/zjZSTU/LightWeightCNN.git
   $ cd LightWeightCNN
   $ mkdocs serve
   

   启动本地服务器后即可登录浏览器localhost:8000

主要维护人员

• zhujian - Initial work - zjZSTU

致谢

引用

@misc{i2016squeezenet,
    title={SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size},
    author={Forrest N. Iandola and Song Han and Matthew W. Moskewicz and Khalid Ashraf and William J. Dally and Kurt Keutzer},
    year={2016},
    eprint={1602.07360},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}

@misc{howard2017mobilenets,
    title={MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications},
    author={Andrew G. Howard and Menglong Zhu and Bo Chen and Dmitry Kalenichenko and Weijun Wang and Tobias Weyand and Marco Andreetto and Hartwig Adam},
    year={2017},
    eprint={1704.04861},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}

@misc{s2018mobilenetv2,
    title={MobileNetV2: Inverted Residuals and Linear Bottlenecks},
    author={Mark Sandler and Andrew Howard and Menglong Zhu and Andrey Zhmoginov and Liang-Chieh Chen},
    year={2018},
    eprint={1801.04381},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}

@misc{zhang2017shufflenet,
    title={ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices},
    author={Xiangyu Zhang and Xinyu Zhou and Mengxiao Lin and Jian Sun},
    year={2017},
    eprint={1707.01083},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}

@misc{ma2018shufflenet,
    title={ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design},
    author={Ningning Ma and Xiangyu Zhang and Hai-Tao Zheng and Jian Sun},
    year={2018},
    eprint={1807.11164},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}

@misc{pascal-voc-2007,
	author = "Everingham, M. and Van~Gool, L. and Williams, C. K. I. and Winn, J. and Zisserman, A.",
	title = "The {PASCAL} {V}isual {O}bject {C}lasses {C}hallenge 2007 {(VOC2007)} {R}esults",
	howpublished = "http://www.pascal-network.org/challenges/VOC/voc2007/workshop/index.html"}

@misc{pascal-voc-2012,
	author = "Everingham, M. and Van~Gool, L. and Williams, C. K. I. and Winn, J. and Zisserman, A.",
	title = "The {PASCAL} {V}isual {O}bject {C}lasses {C}hallenge 2012 {(VOC2012)} {R}esults",
	howpublished = "http://www.pascal-network.org/challenges/VOC/voc2012/workshop/index.html"}

参与贡献方式

欢迎任何人的参与！打开issue或提交合并请求。

注意:

• GIT提交，请遵守Conventional Commits规范
• 语义版本化，请遵守Semantic Versioning 2.0.0规范
• README编写，请遵守standard-readme规范

许可证

Apache License 2.0 © 2020 zjZSTU