# efficientnet-b5-pytorch¶

## Use Case and High-Level Description¶

The efficientnet-b5-pytorch model is one of the EfficientNet models designed to perform image classification. This model was pre-trained in TensorFlow*, then weights were converted to PyTorch*. All the EfficientNet models have been pre-trained 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 3, 456, 456 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-b5-pytorch is the typical object classifier output for the 1000 different classifications matching those in the ImageNet database.

Metric

Value

Type

Classification

GFLOPs

21.252

MParams

30.303

Source framework

PyTorch*

Metric

Original model

Converted model

Top 1

83.69%

83.69%

Top 5

96.71%

96.71%

## Input¶

### Original Model¶

Image, name - data, shape - 1, 3, 456, 456, 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, 456, 456, 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 logits format

### 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 logits format

python3 <omz_dir>/tools/downloader/downloader.py --name <model_name>
python3 <omz_dir>/tools/downloader/converter.py --name <model_name>