face-recognition-resnet100-arcface-onnx

Use Case and High-Level Description

The face-recognition-resnet100-arcface-onnx model is a deep face recognition model with ResNet100 backbone and ArcFace loss. ArcFace is a novel supervisor signal called additive angular margin which used as an additive term in the softmax loss to enhance the discriminative power of softmax loss. This model is pre-trained in MXNet* framework and converted to ONNX* format. More details provided in the paper and repository.

Specification

Metric

Value

Type

Face recognition

GFLOPs

24.2115

MParams

65.1320

Source framework

MXNet*

Accuracy

Metric

Value

LFW accuracy

99.68%

Input

Original Model

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

  • B - batch size

  • C - channel

  • H - height

  • W - width

Channel order is RGB.

Converted Model

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

  • B - batch size

  • C - channel

  • H - height

  • W - width

Channel order is BGR.

Output

Original Model

Face embeddings, name: fc1, 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: fc1, 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 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>

Use Case and High-Level Description

The face-recognition-resnet100-arcface-onnx model is a deep face recognition model with ResNet100 backbone and ArcFace loss. ArcFace is a novel supervisor signal called additive angular margin which used as an additive term in the softmax loss to enhance the discriminative power of softmax loss. This model is pre-trained in MXNet* framework and converted to ONNX* format. More details provided in the paper and repository.

Specification

Metric

Value

Type

Face recognition

GFLOPs

24.2115

MParams

65.1320

Source framework

MXNet*

Accuracy

Metric

Value

LFW accuracy

99.68%

Input

Original Model

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

  • B - batch size

  • C - channel

  • H - height

  • W - width

Channel order is RGB.

Converted Model

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

  • B - batch size

  • C - channel

  • H - height

  • W - width

Channel order is BGR.

Output

Original Model

Face embeddings, name: fc1, 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: fc1, 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 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>

Legal Information

The original model is distributed under the Apache License, Version 2.0. A copy of the license is provided in <omz_dir>/models/public/licenses/APACHE-2.0.txt.