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hbonet-1.0

Use Case and High-Level Description

The hbonet-1.0 model is one of the classification models from repository with width_mult=1.0

Specification

Metric

Value

Type

Classification

GFLOPs

0.305

MParams

4.5447

Source framework

PyTorch*

Accuracy

Metric

Original model

Top 1

73.10%

Top 5

91.00%

Input

Original Model

Image, name: input, shape: 1, 3, 224, 224, format: B, C, H, W, where:

  • B - batch size

  • C - number of channels

  • H - image height

  • W - image width

Expected color order: RGB. Mean values: [123.675, 116.28, 103.53], scale factor for each channel: [58.395, 57.12, 57.375]

Converted Model

Image, name: input, shape: 1, 3, 224, 224, 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, shape: 1, 1000 in B, C format, where:

  • B - batch size

  • C - vector of probabilities for all dataset classes in logits format.

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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