googlenet-v4-tf

Use Case and High-Level Description

The googlenet-v4-tf model is the most recent of the Inception family of models designed to perform image classification. Like the other Inception models, the googlenet-v4-tf model has been pre-trained on the ImageNet image database. For details about this family of models, check out the paper, repository.

Specification

Metric

Value

Type

Classification

GFLOPs

24.584

MParams

42.648

Source framework

TensorFlow*

Accuracy

Metric

Original model

Converted model

Top 1

80.21%

80.21%

Top 5

95.20%

95.20%

Input

Original model

Image, name - input, shape - 1, 299, 299, 3, format is B, H, W, C, where:

  • B - batch size

  • H - height

  • W - width

  • C - channel

Channel order is RGB. Mean values - [127.5, 127.5, 127.5], scale value - 127.5

Converted model

Image, name - data, shape - 1, 299, 299, 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 - InceptionV4/Logits/Predictions, shape - 1, 1001, output data format is B, C, where:

  • B - batch size

  • C - predicted probabilities for each class in [0, 1] range

Converted model

Object classifier according to ImageNet classes, name - InceptionV4/Logits/Predictions, shape - 1, 1001, output data format is B, C, where:

  • B - batch size

  • C - predicted probabilities for each class in [0, 1] range

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: