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.
tecture search becomes increasingly popular in designing efficient mobile-size ConvNets (Tan et al.,2019;Cai et al., 2019), and achieves even better efficiency than hand-crafted mobile ConvNets by extensively tuning the network width, depth, convolution kernel types and sizes. However, it is unclear how to apply these techniques for larger ...
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