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googlenet-v3

Use Case and High-Level Description

The googlenet-v3 model is the first of the Inception family of models designed to perform image classification. For details about this family of models, check out the paper.

Specification

Metric

Value

Type

Classification

GFLOPs

11.469

MParams

23.819

Source framework

TensorFlow*

Accuracy

Metric

Value

Top 1

77.904%

Top 5

93.808%

Input

Original Model

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

  • B - batch size

  • H - image height

  • W - image width

  • C - number of channels

Expected color order: RGB. Mean values: [127.5, 127.5, 127.5], scale factor for each channel: 127.5

Converted Model

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

  • B - batch size

  • H - image height

  • W - image width

  • C - number of channels

Expected color order: BGR.

Output

Object classifier according to ImageNet classes, name: InceptionV3/Predictions/Softmax, shape: 1, 1001 in B, C format, where:

  • B - batch size

  • C - vector of probabilities for all dataset classes in [0, 1] range (0 class is background).

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:

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