Face detector based on MobileNetV2 as a backbone with a multiple SSD head for indoor and outdoor scenes shot by a front-facing camera. During the training of this model, training images were resized to 256x256.
| Metric | Value |
|---|---|
| AP (WIDER) | 86.74% |
| GFlops | 0.786 |
| MParams | 1.828 |
| 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 64 x 64 pixels.
Name: input, shape: [1x3x256x256] - An input image in the format [BxCxHxW],
where:
- B - batch size
- C - number of channels
- H - image height
- W - image width
Expected color order: BGR.
The net outputs blob with shape: [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 (0 - 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.
[*] Other names and brands may be claimed as the property of others.