Face detector based on MobileNetV2 as a backbone with a multiple SSD head for indoor/outdoor scenes shot by a front-facing camera. During training of this model training images were resized to 384x384.
Metric | Value |
---|---|
AP (WIDER) | 91.61% |
GFlops | 1.767 |
MParams | 1.842 |
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: [1x3x384x384] - An input image in the format [BxCxHxW], where:
Expected color order - BGR.
The net outputs a blob with shape: [1, 1, N, 7], where N is the number of detected bounding boxes. For each detection, the description 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 IDconf
- confidence for the predicted classx_min
, y_min
) - coordinates of the top left bounding box cornerx_max
, y_max
) - coordinates of the bottom right bounding box corner.[*] Other names and brands may be claimed as the property of others.