efficientnet-b5

Use Case and High-Level Description

The efficientnet-b5 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

21.252

MParams

30.303

Source framework

TensorFlow*

Accuracy

Metric

Original model

Converted model

Top 1

83.33%

83.33%

Top 5

96.67%

96.67%

Input

Original Model

Image, name - image, shape - 1, 456, 456, 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, 456, 456, 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 the logits format

Converted Model

Object classifier according to ImageNet classes, name - efficientnet-b5/model/head/dense/MatMul, shape - 1, 1000, output data format is B, C, where:

  • B - batch size

  • C - predicted probabilities for each class in the logits format

Download a Model and Convert it into Inference Engine Format

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

An example of using the Model Downloader:

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

An example of using the Model Converter:

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

Use Case and High-Level Description

The efficientnet-b5 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

21.252

MParams

30.303

Source framework

TensorFlow*

Accuracy

Metric

Original model

Converted model

Top 1

83.33%

83.33%

Top 5

96.67%

96.67%

Input

Original Model

Image, name - image, shape - 1, 456, 456, 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, 456, 456, 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 the logits format

Converted Model

Object classifier according to ImageNet classes, name - efficientnet-b5/model/head/dense/MatMul, shape - 1, 1000, output data format is B, C, where:

  • B - batch size

  • C - predicted probabilities for each class in the logits format

Download a Model and Convert it into Inference Engine Format

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

An example of using the Model Downloader:

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

An example of using the Model Converter:

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

Legal Information

The original model is distributed under the Apache License, Version 2.0. A copy of the license is provided in <omz_dir>/models/public/licenses/APACHE-2.0-TF-TPU.txt.