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

  • B - batch size
  • C - number of channels
  • H - image height
  • W - image width

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 (0 class is background). Probabilities are represented in 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 APACHE-2.0-TF-Models.txt.