Convolutional Neural Networks (ConvNets) can be made more accurate by increasing:

  1. depth
  2. width
  3. resolution

Scaling up any of these dimensions requires more processing power (FLOPS).

This paper suggests that these dimensions are interdependent, and scaling all of them in a balanced manner will lead to better accuracy while using less resources. The paper suggests a way to perform such scaling, known as a compound coefficient. The paper then uses neural architecture search to design a baseline model, then scale it up using compound coefficient to obtain a family of networks known as EfficientNet.

Scaling each of the dimensions is a way to gain accuracy, but scaling too much leads to difficulties in training. The accuracy gains also diminishes quickly (for the amount of resources required).

Experiments in this paper shows that scaling just width without changing depth or resolution leads to accuracy saturating quickly. With a deeper network and higher resolution, the width scaling results in higher accuracy with the same cost.

The compound coefficient uniformly scales depth, width, and resolution. It can be thought of as how much resources can be dedicated to scale the model.

EfficientNet family of models (B0 to B7) was found by: