(DL Classification)Review: EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Ref Tensorflow EfficientNet Pytorch EfficientNet and Pretrained 根据上图所示，分类网络模型往往是在width, height, resolution三个方向进行设计，所以作者想到，对三个因素都进行调整是否能得到一个更好的模型 Compound Model Scaling Width, Depth, Resolution分别对网络模型的
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相关文章
- 1. EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks（2019）
- 2. EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
- 3. [ICML19]EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
- 4. #Paper Reading# EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
- 5. EfficientNet 《EfficientNet:Rethinking model scaling for convolutional neural networks》
- 6. 论文阅读：EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
- 7. [论文解读]ICML 2019|EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
- 8. EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks (2019)论文笔记
- 9. 【论文解读】EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
- 10. EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks 论文笔记