efficientnet-b7-pytorch

## Use Case and High-Level Description

The efficientnet-b7-pytorch model is one of the EfficientNet models designed to perform image classification. This model was pretrained in TensorFlow*, then weights were converted to PyTorch*. All the EfficientNet models have been pretrained on the ImageNet* image database. For details about this family of models, check out the EfficientNets for PyTorch repository.

The model input is a blob that consists of a single image with the [3x600x600] shape in the RGB order. Before passing the image blob to the network, do the following:

1. Subtract the RGB mean values as follows: [123.675,116.28,103.53]
2. Divide the RGB mean values by [58.395,57.12,57.375]

The model output for efficientnet-b7-pytorch is the typical object classifier output for the 1000 different classifications matching those in the ImageNet database.

## Specification

Metric Value
Type Classification
GFLOPs 77.618
MParams 66.193
Source framework PyTorch*

## Accuracy

Metric Original model Converted model
Top 1 84.42% 84.42%
Top 5 96.91% 96.91%

## Input

### Original Model

Image, name - data, shape - 1,3,600,600, format is B,C,H,W where:

• B - batch size
• C - channel
• H - height
• W - width

Channel order is RGB. Mean values - [123.675,116.28,103.53], scale values - [58.395,57.12,57.375].

### Converted Model

Image, name - data, shape - 1,3,600,600, format is B,C,H,W where:

• B - batch size
• C - channel
• H - height
• W - width

Channel order is BGR.

## Output

### Original Model

Object classifier according to ImageNet classes, name - prob, shape - 1,1000, output data format is B,C where:

• B - batch size
• C - predicted probabilities for each class in the [0, 1] range

### Converted Model

Object classifier according to ImageNet classes, name - prob, shape - 1,1000, output data format is B,C where:

• B - batch size
• C - predicted probabilities for each class in the [0, 1] range