facenet-20180408-102900

Use Case and High-Level Description

FaceNet: A Unified Embedding for Face Recognition and Clustering. For details see the repository, paper

Specification

Metric

Value

Type

Face recognition

GFlops

2.846

MParams

23.469

Source framework

TensorFlow*

Accuracy

Metric

Value

LFW accuracy

99.14%

Input

Original model

  1. Image, name - batch_join:0, shape - 1, 160, 160, 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 - 128.0

  2. A boolean input, manages state of the graph (train/infer), name - phase_train, shape - 1.

Converted model

Image, name - image_batch/placeholder_port_0, shape - 1, 3, 160, 160, 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

Vector of floating-point values - face embeddings, Name - embeddings.

Converted model

Face embeddings, name - InceptionResnetV1/Bottleneck/BatchNorm/Reshape_1/Normalize, in format B,C, where:

  • B - batch size

  • C - row-vector of 512 floating-point values - face embeddings

Download a Model and Convert it into Inference Engine Format

You can download models and if necessary convert them into Inference Engine format using the Model Downloader and other automation tools as shown in the examples below.

An example of using the Model Downloader:

python3 <omz_dir>/tools/downloader/downloader.py --name <model_name>

An example of using the Model Converter:

python3 <omz_dir>/tools/downloader/converter.py --name <model_name>