Google’s EfficientNet: Improving Accuracy and Efficiency through AutoML and Model Scaling

Google has earned a reputation for pushing out new AI technologies and upgrades at a remarkable pace and their announcement of EfficientNet serves as the latest example.  Leveraging their work with AutoML, Google’s scientists employed a scaling method that offers up to a tenfold increase in network efficiency.

The company writes: “Convolutional neural networks (CNNs) are commonly developed at a fixed resource cost, and then scaled up in order to achieve better accuracy when more resources are made available. For example, ResNet can be scaled up from ResNet-18 to ResNet-200 by increasing the number of layers, and recently, GPipe achieved 84.3% ImageNet top-1 accuracy by scaling up a baseline CNN by a factor of four. The conventional practice for model scaling is to arbitrarily increase the CNN depth or width, or to use larger input image resolution for training and evaluation. While these methods do improve accuracy, they usually require tedious manual tuning, and still often yield suboptimal performance. What if, instead, we could find a more principled method to scale up a CNN to obtain better accuracy and efficiency?In our ICML 2019 paper,

“EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks”, we propose a novel model scaling method that uses a simple yet highly effective compound coefficient to scale up CNNs in a more structured manner. Unlike conventional approaches that arbitrarily scale network dimensions, such as width, depth and resolution, our method uniformly scales each dimension with a fixed set of scaling coefficients. Powered by this novel scaling method and recent progress on AutoML, we have developed a family of models, called EfficientNets, which superpass state-of-the-art accuracy with up to 10x better efficiency (smaller and faster).

Compound Model Scaling: A Better Way to Scale Up CNNs
In order to understand the effect of scaling the network, we systematically studied the impact of scaling different dimensions of the model. While scaling individual dimensions improves model performance, we observed that balancing all dimensions of the network—width, depth, and image resolution—against the available resources would best improve overall performance.

The first step in the compound scaling method is to perform a grid search to find the relationship between different scaling dimensions of the baseline network under a fixed resource constraint (e.g., 2x more FLOPS).This determines the appropriate scaling coefficient for each of the dimensions mentioned above. We then apply those coefficients to scale up the baseline network to the desired target model size or computational budget.