[Notes] EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks (May 2019)

This paper was published by Mingxing Tan and Quoc V. Le, members of Google Research, Brain Team. They propose a method to scale up neural nets. Their biggest model achieves 84.4% top-1 / 97.1% top-5 accuracy on ImageNet and the state-of-the-art on CIFAR-100, while being 8.4x smaller and 6.1x faster on inference than the best existing ConvNet.

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