mobilenet-v1-1.0-224

Use Case and High-Level Description

mobilenet-v1-1.0-224 is one of MobileNet V1 architecture with the width multiplier 1.0 and resolution 224. It is small, low-latency, low-power models parameterized to meet the resource constraints of a variety of use cases. They can be built upon for classification, detection, embeddings and segmentation similar to how other popular large scale models are used.

Specification

Metric

Value

Type

Classification

GFlops

1.148

MParams

4.221

Source framework

Caffe*

Accuracy

Metric

Value

Top 1

69.496%

Top 5

89.224%

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: BGR. Mean values - [103.94, 116.78, 123.68], scale factor for each channel - 58.8235294117647

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

Original model

Object classifier according to ImageNet classes, name - prob, shape - 1, 1000, 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 - prob, shape - 1, 1000, 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 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: