EfficientNet Explained: Rethinking Model Scaling for Convolutional Neural Networks 

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
EfficientNet is a convolutional neural network architecture and scaling method that uniformly scales all dimensions of depth/width/resolution using a compound coefficient.
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The researchers used the compound scaling method to scale the dimensions of the network. The applied grid search strategy to find the relationship between the different scaling dimensions of the baseline network under a fixed resource constraint. Using this strategy, the could find the appropriate scaling coefficients for each of the dimensions to be scaled-up. Using these coefficients, the baseline network was scaled by the desired size.
What does scaling mean in the context of CNNs?
There are three scaling dimensions of a CNN: depth, width, and resolution.
Depth simply means how deep the networks is which is equivalent to the number of layers in it.
Width simply means how wide the network is. One measure of width, for example, is the number of channels in a Conv layer
Resolution is simply the image resolution that is being passed to a CNN.
Compound scaling:
Compound scaling method uses a compound co-efficient ø to scale width, depth, and resolution together
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