anti-spoof-mn3#

Use Case and High-Level Description#

The anti-spoof-mn3 model is an anti-spoofing binary classifier based on the MobileNetV3, trained on the CelebA-Spoof dataset. It’s a small, light model, trained to predict whether or not a spoof RGB image given to the input. A lot of advanced techniques have been tried and selected the best suit options for the task. For details see original repository.

Specification#

Metric

Value

Type

Classification

GFlops

0.15

MParams

3.02

Source framework

PyTorch*

Accuracy#

Metric

Original model

Converted model

ACER

3.81%

3.81%

Input#

Original Model#

Image, name: actual_input_1, shape: 1, 3, 128, 128, 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: [151.2405, 119.5950, 107.8395], scale factor: [63.0105, 56.4570, 55.0035]

Converted Model#

Image, name: actual_input_1, shape: 1, 3, 128, 128, 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#

Probabilities for two classes (0 class is a real person, 1 - is a spoof image). Name: output1 Shape: 1, 2, format: B, C, where:

  • B - batch size

  • C - vector of probabilities.

Converted model#

Probabilities for two classes (0 class is a real person, 1 - is a spoof image). Name: output1 Shape: 1, 2, 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: