efficientnet-b0#

Use Case and High-Level Description#

The efficientnet-b0 model is one of the EfficientNet models designed to perform image classification. This model was pre-trained in TensorFlow*. All the EfficientNet models have been pre-trained on the ImageNet image database. For details about this family of models, check out the TensorFlow Cloud TPU repository.

Specification#

Metric

Value

Type

Classification

GFLOPs

0.819

MParams

5.268

Source framework

TensorFlow*

Accuracy#

Metric

Original model

Converted model

Top 1

75.70%

75.70%

Top 5

92.76%

92.76%

Input#

Original Model#

Image, name - image, shape - 1, 224, 224, 3, format is B, H, W, C, where:

  • B - batch size

  • H - height

  • W - width

  • C - channel

Channel order is RGB.

Converted Model#

Image, name - sub/placeholder_port_0, shape - 1, 224, 224, 3, format is B, H, W, C, where:

  • B - batch size

  • H - height

  • W - width

  • C - channel

Channel order is BGR.

Output#

Original Model#

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

  • B - batch size

  • C - predicted probabilities for each class in logits format

Converted Model#

Object classifier according to ImageNet classes, name - efficientnet-b0/model/head/dense/MatMul, shape - 1, 1000, output data format is B, C, where:

  • B - batch size

  • C - predicted probabilities for each class in logits format

Download a Model and Convert it into OpenVINO™ IR Format#

You can download models and if necessary convert them into OpenVINO™ IR format using the Model Downloader and other automation tools as shown in the examples below.

An example of using the Model Downloader:

omz_downloader --name <model_name>

An example of using the Model Converter:

omz_converter --name <model_name>

Demo usage#

The model can be used in the following demos provided by the Open Model Zoo to show its capabilities: