anti-spoof-mn3¶
Use Case and High-Level Description¶
The anti-spoof-mn3
model is an anti-spoofing binary classifier based on the MobileNetV3, trained on the CelebA-Spoof dataset. It’s a small, light model, trained to predict whether or not a spoof RGB image given to the input. A lot of advanced techniques have been tried and selected the best suit options for the task. For details see original repository.
Specification¶
Metric |
Value |
---|---|
Type |
Classification |
GFlops |
0.15 |
MParams |
3.02 |
Source framework |
PyTorch* |
Accuracy¶
Metric |
Original model |
Converted model |
---|---|---|
ACER |
3.81% |
3.81% |
Input¶
Original Model¶
Image, name: actual_input_1
, shape: 1, 3, 128, 128
, format: B, C, H, W
, where:
B
- batch sizeC
- number of channelsH
- image heightW
- image width
Expected color order: RGB
. Mean values: [151.2405, 119.5950, 107.8395], scale factor: [63.0105, 56.4570, 55.0035]
Converted Model¶
Image, name: actual_input_1
, shape: 1, 3, 128, 128
, format: B, C, H, W
, where:
B
- batch sizeC
- number of channelsH
- image heightW
- image width
Expected color order: BGR
.
Output¶
Original model¶
Probabilities for two classes (0 class is a real person, 1 - is a spoof image). Name: output1
Shape: 1, 2
, format: B, C
, where:
B
- batch sizeC
- vector of probabilities.
Converted model¶
Probabilities for two classes (0 class is a real person, 1 - is a spoof image). Name: output1
Shape: 1, 2
, format: B, C
, where:
B
- batch sizeC
- vector of probabilities.
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 MIT License.