face-detection-retail-0005#
Use Case and High-Level Description#
Face detector based on MobileNetV2 as a backbone with a single SSD head for indoor/outdoor scenes shot by a front-facing camera. The single SSD head from 1/16 scale feature map has nine clustered prior boxes.
Example#
Specification#
Metric |
Value |
---|---|
AP (WIDER) |
84.52% |
GFlops |
0.982 |
MParams |
1.021 |
Source framework |
PyTorch* |
Average Precision (AP) is defined as an area under the precision/recall curve. All numbers were evaluated by taking into account only faces bigger than 60 x 60 pixels.
Inputs#
Image, name: input.1
, shape: 1, 3, 300, 300
in the format B, C, H, W
, where:
B
- batch sizeC
- number of channelsH
- image heightW
- image width
Expected color order: BGR
.
Outputs#
The net outputs blob with shape: 1, 1, 200, 7
in the format 1, 1, N, 7
, where N
is the number of detected
bounding boxes. Each detection has the format [image_id
, label
, conf
, x_min
, y_min
, x_max
, y_max
], where:
image_id
- ID of the image in the batchlabel
- predicted class ID (1 - face)conf
- confidence for the predicted class(
x_min
,y_min
) - coordinates of the top left bounding box corner(
x_max
,y_max
) - coordinates of the bottom right bounding box corner
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.