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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, 3, 299, 299, format is B, C, H, W, where:

  • B - batch size

  • C - channel

  • H - height

  • W - width

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 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>
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