face-detection-adas-0001#
Use Case and High-Level Description#
Face detector for driver monitoring and similar scenarios. The network features a default MobileNet backbone that includes depth-wise convolutions to reduce the amount of computation for the 3x3 convolution block.
Example#
Specification#
Metric |
Value |
---|---|
AP (head height >10px) |
37.4% |
AP (head height >32px) |
84.8% |
AP (head height >64px) |
93.1% |
AP (head height >100px) |
94.1% |
Min head size |
90x90 pixels on 1080p |
GFlops |
2.835 |
MParams |
1.053 |
Source framework |
Caffe* |
Average Precision (AP) is defined as an area under the precision/recall curve. Numbers are on Wider Face validation subset.
Inputs#
Image, name: data
, shape: 1, 3, 384, 672
in the format B, C, H, W
, where:
B
- batch sizeC
- number of channelsH
- image heightW
- image width
Expected color order is 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. The results are sorted by confidence in decreasing order. 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.