Paper Reading：ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design
ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design Motivation 以往的移動端的CNN設計在考慮計算節省時都直接致力於優化整體網絡計算所需的Flops。但實際上一個網絡模型的訓練或推理過程Flops等計算只是其時間的一部分，其它像內存讀寫/外部數據IO操作等都會佔不小比例的時間。所以不應只限於去
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相關文章
- 1. ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design
- 2. ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design
- 3. 【論文閱讀筆記】ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design
- 4. 論文筆記：ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design
- 5. 輕量化網絡（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