Face detector based on SqueezeNet light (half-channels) as a backbone with a single SSD for indoor/outdoor scenes shot by a front-facing camera. The backbone consists of fire modules to reduce the number of computations. The single SSD head from 1/16 scale feature map has nine clustered prior boxes.
| Metric | Value |
|---|---|
| AP (WIDER) | 83.00% |
| GFlops | 1.067 |
| MParams | 0.588 |
| Source framework | Caffe* |
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.
Image, name: data, 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.
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
The model can be used in the following demos provided by the Open Model Zoo to show its capabilities:
- Face Recognition Python* Demo
- Gaze Estimation Demo
- G-API Gaze Estimation Demo
- Interactive Face Detection C++ Demo
- G-API Interactive Face Detection Demo
- Multi-Channel Face Detection C++ Demo
- Object Detection C++ Demo
- Object Detection Python* Demo
[*] Other names and brands may be claimed as the property of others.