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.
ference. Compared to the widely used ResNet-50 (He et al., 2016), our EfficientNet-B4 improves the top-1 accuracy from 76.3% to 83.0% (+6.7%) with similar FLOPS. Besides ImageNet, EfficientNets also transfer well and achieve state-of-the-art accuracy on 5 out of 8 widely used datasets, while reducing parameters by up to 21x than existing ...
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