EfficientNet is a family of CNN architectures that uses a principled compound scaling method to uniformly scale network depth, width, and resolution — achieving state-of-the-art accuracy at each efficiency level from mobile to server-scale.

What Is EfficientNet?

• Baseline: EfficientNet-B0 found by NAS (MnasNet-like search).
• Compound Scaling: Jointly scale depth ($d = alpha^phi$), width ($w = eta^phi$), and resolution ($r = gamma^phi$) where $alpha cdot eta^2 cdot gamma^2 approx 2$.
• Family: B0 through B7 (scaling factor $phi$ from 0 to 6).
• Paper: Tan & Le (2019).

Why It Matters

• Principled Scaling: First to show that balanced scaling of all three dimensions outperforms scaling any one alone.
• Efficiency: EfficientNet-B3 matches ResNet-152 accuracy with 8× fewer FLOPs.
• Standard: Became the default CNN backbone for many vision tasks (2019-2021).

EfficientNet is the science of neural network scaling — proving that balanced growth in depth, width, and resolution is the key to efficient accuracy.