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 448x448.
| Metric | Value |
|---|---|
| AP (WIDER) | 92.74% |
| GFlops | 2.406 |
| MParams | 1.851 |
| 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: [1x3x448x448] - 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 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 batch
- `label` - predicted class ID
- `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.