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face-detection-retail-0005

Use Case and High-Level Description

Face detector based on MobileNetV2 as a backbone with a single SSD head for indoor/outdoor scenes shot by a front-facing camera. The single SSD head from 1/16 scale feature map has nine clustered prior boxes.

Example

Specification

Metric

Value

AP (WIDER)

84.52%

GFlops

0.982

MParams

1.021

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 60 x 60 pixels.

Inputs

Image, name: input.1, shape: 1, 3, 300, 300 in the format B, C, H, W, where:

  • B - batch size

  • C - number of channels

  • H - image height

  • W - 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 batch

  • label - 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

Demo usage

The model can be used in the following demos provided by the Open Model Zoo to show its capabilities:

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