Efficientnet

olympus.models.efficientnet.delayed_geffnet(name)

Convolutional Neural Networks (ConvNets) are commonly developed at a fixed resource budget, and then scaled up for better accuracy if more resources are available. In this paper, we systematically study model scaling and identify that carefully balancing network depth, width, and resolution can lead to better performance. Based on this observation, we propose a new scaling method that uniformly scales all dimensions of depth/width/resolution using a simple yet highly effective compound coefficient. We demonstrate the effectiveness of this method on scaling up MobileNets and ResNet. More on arxiv

Source code available at github

References

[1]	Mingxing Tan, Quoc V. Le. “EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks”, Jun 2019

Attributes:

input_size: (2, 224, 224)

expected sample size