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 OpenVINO™ IR Format

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omz_downloader --name <model_name>

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Demo usage

The model can be used in the following demos provided by the Open Model Zoo to show its capabilities: