EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

ICML 2019 Mingxing TanQuoc V. Le

Convolutional Neural Networks (ConvNets) are commonly developed at a fixed resource budget, and then scaled up for better accuracy if more resources are available. In this paper, we systematically study model scaling and identify that carefully balancing network depth, width, and resolution can lead to better performance... (read more)

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Evaluation results from the paper


 SOTA for Image Classification on CIFAR-100 (using extra training data)

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Task Dataset Model Metric name Metric value Global rank Uses extra
training data
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Image Classification CIFAR-10 EfficientNet Percentage correct 98.9 # 2
Image Classification CIFAR-100 EfficientNet Percentage correct 91.7 # 1
Image Classification Flowers-102 EfficientNet Accuracy 98.8% # 1
Image Classification ImageNet EfficentNet Top 1 Accuracy 84.4% # 2
Image Classification ImageNet EfficentNet Top 5 Accuracy 97.1% # 2
Image Classification Stanford Cars EfficientNet Accuracy 94.7% # 1