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What is: EfficientNet?

SourceEfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Year2000
Data SourceCC BY-SA - https://paperswithcode.com

EfficientNet is a convolutional neural network architecture and scaling method that uniformly scales all dimensions of depth/width/resolution using a compound coefficient. Unlike conventional practice that arbitrary scales these factors, the EfficientNet scaling method uniformly scales network width, depth, and resolution with a set of fixed scaling coefficients. For example, if we want to use 2N2^N times more computational resources, then we can simply increase the network depth by αN\alpha ^ N, width by βN\beta ^ N, and image size by γN\gamma ^ N, where α,β,γ\alpha, \beta, \gamma are constant coefficients determined by a small grid search on the original small model. EfficientNet uses a compound coefficient ϕ\phi to uniformly scales network width, depth, and resolution in a principled way.

The compound scaling method is justified by the intuition that if the input image is bigger, then the network needs more layers to increase the receptive field and more channels to capture more fine-grained patterns on the bigger image.

The base EfficientNet-B0 network is based on the inverted bottleneck residual blocks of MobileNetV2, in addition to squeeze-and-excitation blocks.

EfficientNets also transfer well and achieve state-of-the-art accuracy on CIFAR-100 (91.7%), Flowers (98.8%), and 3 other transfer learning datasets, with an order of magnitude fewer parameters.