mobilenet-v1-1.0-224-tf#

Use Case and High-Level Description#

mobilenet-v1-1.0-224 is one of MobileNets - 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. For details, see the paper.

Specification#

Metric

Value

Type

Classification

GFlops

1.148

MParams

4.222

Source framework

TensorFlow*

Accuracy#

Metric

Value

Top 1

71.03%

Top 5

89.94%

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#

Probabilities for all dataset classes in [0, 1] range (0 class is background). Name: MobilenetV1/Predictions/Reshape_1.

Converted Model#

Probabilities for all dataset classes in [0, 1] range (0 class is background). Name: MobilenetV1/Predictions/Softmax, 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: