mobilenet-v1-0.25-128#

Use Case and High-Level Description#

mobilenet-v1-0.25-128 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 paper.

Specification#

Metric

Value

Type

Classification

GFlops

0.028

MParams

0.468

Source framework

TensorFlow*

Accuracy#

Metric

Value

Top 1

40.54%

Top 5

65%

Input#

Original Model#

Image, name: input, shape: 1, 128, 128, 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, 128, 128, 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: