EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks EfficientNet is a family of convolutional neural networks (ConvNets) that achieve state-of-the-art accuracy and efficiency. The paper introduces a new scaling method that uniformly scales depth, width, and resolution using a compound coefficient. This method outperforms previous approaches by balancing all three dimensions, leading to better accuracy and efficiency. The authors demonstrate the effectiveness of this method on MobileNets and ResNet, and use neural architecture search to design a new baseline network, which is then scaled to create EfficientNets. EfficientNet-B7 achieves 84.3% top-1 accuracy on ImageNet, being 8.4x smaller and 6.1x faster than the best existing ConvNet. EfficientNets also transfer well to other datasets, achieving state-of-the-art accuracy with significantly fewer parameters. The paper also discusses the importance of balancing network dimensions and the benefits of compound scaling for improving accuracy and efficiency. The results show that EfficientNets outperform existing models in terms of accuracy and efficiency, making them a promising solution for a wide range of applications.EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks EfficientNet is a family of convolutional neural networks (ConvNets) that achieve state-of-the-art accuracy and efficiency. The paper introduces a new scaling method that uniformly scales depth, width, and resolution using a compound coefficient. This method outperforms previous approaches by balancing all three dimensions, leading to better accuracy and efficiency. The authors demonstrate the effectiveness of this method on MobileNets and ResNet, and use neural architecture search to design a new baseline network, which is then scaled to create EfficientNets. EfficientNet-B7 achieves 84.3% top-1 accuracy on ImageNet, being 8.4x smaller and 6.1x faster than the best existing ConvNet. EfficientNets also transfer well to other datasets, achieving state-of-the-art accuracy with significantly fewer parameters. The paper also discusses the importance of balancing network dimensions and the benefits of compound scaling for improving accuracy and efficiency. The results show that EfficientNets outperform existing models in terms of accuracy and efficiency, making them a promising solution for a wide range of applications.