# 🕸️ EfficientNet#

Category of Task: Vision

Kind of Task: Image Classification

## Overview#

The EfficientNet model family is a set of convolutional neural networks that can be used as the basis for a variety of vision tasks, although they were initially designed for image classification. The model family was designed to reach the highest accuracy for a given computation budget during inference by simultaneously scaling model depth, model width, and image resolution according to an empirically determined scaling law.

Paper: EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks by Mingxing Tan and Quoc V. Le

Code: gen-efficientnet-pytorch Github repository by Ross Wightman

Hyperparameters: DeepLearningExamples Github repository by Nvidia

## Architecture#

The table below from Tan and Le specifies the EfficientNet baseline architecture broken up into separate stages. MBConv indicates a mobile inverted bottleneck with a specific expansion size and kernel size. Resolution is the expected input resolution of the current stage. Number of channels is the number of output channels of the current stage. Number of layers indicates the number of repeated blocks in each stage. Subsequent EfficientNet family members scale the resolution, number of channels, and number of layers according to the resolution, width, and depth scaling parameters defined by Tan and Le.

## Family members#

Tan and Le included 8 members in their model family. The goal was for each family member to have approximately double the FLOPs of the previous family member. Currently, we only support EfficientNet-B0.

Model Family Member

Parameter Count

TPU Repo Accuracy*

Our Accuracy**

Training Time on 8x3080

EfficientNet-B0

5.3M

77.1%

77.22%

23.3 hr

EfficientNet-B1

7.8M

79.1%

TBA

TBA

EfficientNet-B2

9.2M

80.1%

TBA

TBA

EfficientNet-B3

12M

81.6%

TBA

TBA

EfficientNet-B4

19M

82.9%

TBA

TBA

EfficientNet-B5

30M

83.6%

TBA

TBA

EfficientNet-B6

43M

84.0%

TBA

TBA

EfficientNet-B7

66M

84.3%

TBA

TBA

*Includes label smoothing, sample-wise stochastic depth, and AutoAugment

**Includes label smoothing and sample-wise stochastic depth

## Default Training Hyperparameters#

Our default hyperparameters are identical to the Nvidia Deep Learning Examples except:

• Applying weight decay to batch normalization trainable parameters

• Batch normalization parameters are momentum = 0.1 and eps = 1e-5