Edit this page

hbonet-0.5

Use Case and High-Level Description

The hbonet-0.5 model is one of the classification models from repository with width_mult=0.5

Specification

Metric

Value

Type

Classification

GFLOPs

0.096

MParams

2.5289

Source framework

PyTorch*

Accuracy

Metric

Original model

Top 1

67.00%

Top 5

86.90%

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.

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>
Prev Next