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mobilenet-v2-1.0-224

Use Case and High-Level Description

mobilenet-v2-1.0-224 is one of MobileNet models, which are small, low-latency, low-power, and parameterized to meet the resource constraints of a variety of use cases. They can be used for classification, detection, embeddings, and segmentation like other popular large-scale models. For details, see the paper.

Specification

Metric

Value

Type

Classification

GFlops

0.615

MParams

3.489

Source framework

TensorFlow*

Accuracy

Metric

Value

Top 1

71.85%

Top 5

90.69%

Input

Original Model

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

  • B - batch size

  • H - image height

  • W - image width

  • C - number of channels

Expected color order: BGR.

Output

Original Model

Name: MobilenetV2/Predictions/Reshape_1. Probabilities for all dataset classes in [0, 1] range (0 class is background).

Converted Model

Name: MobilenetV2/Predictions/Softmax. Probabilities for all dataset classes in [0, 1] range (0 class is background). Shape: 1, 1001, format: B, C, where:

  • B - batch size

  • C - vector of probabilities.

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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