Transcription of EfficientNet: Rethinking Model Scaling for Convolutional …
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EfficientNet: Rethinking Model Scaling for Convolutional …
efficientnet : Rethinking Model Scaling for Convolutional Neural NetworksMingxing Tan1 Quoc V. Le1 AbstractConvolutional Neural Networks (ConvNets) arecommonly developed at a fixed resource budget,and then scaled up for better accuracy if moreresources are available. In this paper, we sys-tematically study Model Scaling and identify thatcarefully balancing network depth, width, and res-olution can lead to better performance. Basedon this observation, we propose a new scalingmethod that uniformly scales all dimensions ofdepth/width/resolution using a simple yet highlyeffectivecompound coefficient. We demonstratethe effectiveness of this method on Scaling upMobileNets and go even further, we use neural architec-ture search to design a new baseline networkand scale it up to obtain a family of models,calledEfficientNets,which achieve muchbetter accuracy and efficiency than previousConvNets.
model scaling heavily depends on the baseline network; to go even further, we use neural architecture search (Zoph & Le,2017;Tan et al.,2019) to develop a new baseline network, and scale it up to obtain a family of models, called EfficientNets. Figure1summarizes the ImageNet perfor-mance, where our EfficientNets significantly outperform
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