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.
tuning cost. In this paper, we aim to study model efficiency for super large ConvNets that surpass state-of-the-art accu-racy. To achieve this goal, we resort to model scaling. Model Scaling: There are many ways to scale a Con-vNet for different resource constraints: ResNet (He et al., 2016) can be scaled down (e.g., ResNet-18) or up (e.g.,
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