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

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 Converter:

## Legal Information

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