Sphereface

Use Case and High-Level Description

Deep face recognition under open-set protocol

Specification

Metric

Value

Type

Face recognition

GFLOPs

3.504

MParams

22.671

Source framework

Caffe*

Accuracy

Metric

Value

LFW accuracy

98.8321%

Input

Original model

Image, name - data, shape - 1, 3, 112, 96, format is B, C, H, W, where:

  • B - batch size

  • C - channel

  • H - height

  • W - width

Channel order is BGR. Mean values - [127.5, 127.5, 127.5], scale value - 128

Converted model

Image, name - data, shape - 1, 3, 112, 96, format is B, C, H, W, where:

  • B - batch size

  • C - channel

  • H - height

  • W - width

Channel order is BGR.

Output

Original model

Face embeddings, name - fc5, shape - 1, 512, output data format - B, C, where:

  • B - batch size

  • C - row-vector of 512 floating points values, face embeddings

The net outputs on different images are comparable in cosine distance.

Converted model

Face embeddings, name - fc5, shape - 1, 512, output data format - B, C, where:

  • B - batch size

  • C - row-vector of 512 floating points values, face embeddings

The net outputs on different images are comparable in cosine distance.

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: