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: