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 sizeC
- channelH
- heightW
- width
Channel order is RGB
.
Converted Model¶
Image, name: data
, shape: 1, 3, 112, 112
, format: B, C, H, W
, where:
B
- batch sizeC
- channelH
- heightW
- width
Channel order is BGR
.
Output¶
Original Model¶
Face embeddings, name: fc1
, shape: 1, 512
, output data format: B, C
, where:
B
- batch sizeC
- 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 sizeC
- 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:
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
.