Paper Reading：ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design

时间 2021-01-02

标签 computer vision DeepLearning Model compression 繁體版

原文原文链接

ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design Motivation 以往的移动端的CNN设计在考虑计算节省时都直接致力于优化整体网络计算所需的Flops。但实际上一个网络模型的训练或推理过程Flops等计算只是其时间的一部分，其它像内存读写/外部数据IO操作等都会占不小比例的时间。所以不应只限于去

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1. ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design
2. ShuﬄeNet V2: Practical Guidelines for Eﬃcient CNN Architecture Design
3. 【论文阅读笔记】ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design
4. 论文笔记：ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design
5. 轻量化网络（七）ShuﬄeNet V2: Practical Guidelines for Eﬃcient CNN Architecture Design
6. [paper] Inception v2/v3
7. ShuffleNet V2
8. Architecture Design Guide
9. Paper Reading：MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
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1. ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design
2. ShuﬄeNet V2: Practical Guidelines for Eﬃcient CNN Architecture Design
3. 【论文阅读笔记】ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design
4. 论文笔记：ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design
5. 轻量化网络（七）ShuﬄeNet V2: Practical Guidelines for Eﬃcient CNN Architecture Design
6. [paper] Inception v2/v3
7. ShuffleNet V2
8. Architecture Design Guide
9. Paper Reading：MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
10. Path-Level Network Transformation for Efficient Architecture Search

>>更多相关文章<<