face-detection-0202#
Use Case and High-Level Description#
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 384x384.
Example#
Specification#
Metric |
Value |
---|---|
AP (WIDER) |
91.94% |
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.
Inputs#
Image, name: image
, shape: 1, 3, 384, 384
in the format B, C, H, W
, where:
B
- batch sizeC
- number of channelsH
- image heightW
- image width
Expected color order: 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. 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
Training Pipeline#
The OpenVINO Training Extensions provide a training pipeline, allowing to fine-tune the model on custom dataset.
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.