landmarks-regression-retail-0009#
Use Case and High-Level Description#
This is a lightweight landmarks regressor for the Smart Classroom scenario. It has a classic convolutional design: stacked 3x3 convolutions, batch normalizations, PReLU activations, and poolings. Final regression is done by the global depthwise pooling head and FullyConnected layers. The model predicts five facial landmarks: two eyes, nose, and two lip corners.
Example#
Specification#
Metric |
Value |
---|---|
Mean Normed Error (on VGGFace2) |
0.0705 |
Face location requirements |
Tight crop |
GFlops |
0.021 |
MParams |
0.191 |
Source framework |
PyTorch* |
Normed Error (NE) for ith sample has the following form:
where N is the number of landmarks, p-hat and p are, correspondingly, the prediction and ground truth vectors of kth landmark of ith sample, and di is the interocular distance for ith sample.
Inputs#
Image, name: 0
, shape: 1, 3, 48, 48
in the format B, C, H, W
, where:
B
- batch sizeC
- number of channelsH
- image heightW
- image width
The expected color order is BGR
.
Outputs#
The net outputs a blob with the shape: 1, 10, 1, 1
, containing a row-vector of 10 floating point values
for five landmarks coordinates in the form (x0, y0, x1, y1, …, x4, y4).
All the coordinates are normalized to be in range [0, 1].
Demo usage#
The model can be used in the following demos provided by the Open Model Zoo to show its capabilities:
Legal Information#
[*] Other names and brands may be claimed as the property of others.