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 architecturesearch to design a new baseline network and scaleit up to obtain a family of models, calledEfficient-Nets, which achieve much better accuracy and effi-ciency than previous ConvNets. In particular, ourEfficientNet-B7 achieves state-of-the-art / top-5 accuracy on ImageNet, fasteron inferencethan the best existing ConvNet.
Figure 1. Model Size vs. ImageNet Accuracy. All numbers are for single-crop, single-model. Our EfficientNets significantly out-perform other ConvNets. In particular, EfficientNet-B7 achieves new state-of-the-art 84.4% top-1 accuracy but being 8.4x smaller and 6.1x faster than GPipe. EfficientNet-B1 is 7.6x smaller and 5.7x faster than ...
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