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 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
.