face-detection-adas-0001

Use Case and High-Level Description

Face detector for driver monitoring and similar scenarios. The network features a default MobileNet backbone that includes depth-wise convolutions to reduce the amount of computation for the 3x3 convolution block.

Example

_images/face-detection-adas-0001.png

Specification

Metric

Value

AP (head height >10px)

37.4%

AP (head height >32px)

84.8%

AP (head height >64px)

93.1%

AP (head height >100px)

94.1%

Min head size

90x90 pixels on 1080p

GFlops

2.835

MParams

1.053

Source framework

Caffe*

Average Precision (AP) is defined as an area under the precision/recall curve. Numbers are on Wider Face validation subset.

Inputs

Image, name: input, shape: 1, 3, 384, 672 in the format B, C, H, W, where:

  • B - batch size

  • C - number of channels

  • H - image height

  • W - image width

Expected color order is 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. The results are sorted by confidence in decreasing order. 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

Use Case and High-Level Description

Face detector for driver monitoring and similar scenarios. The network features a default MobileNet backbone that includes depth-wise convolutions to reduce the amount of computation for the 3x3 convolution block.

Example

_images/face-detection-adas-0001.png

Specification

Metric

Value

AP (head height >10px)

37.4%

AP (head height >32px)

84.8%

AP (head height >64px)

93.1%

AP (head height >100px)

94.1%

Min head size

90x90 pixels on 1080p

GFlops

2.835

MParams

1.053

Source framework

Caffe*

Average Precision (AP) is defined as an area under the precision/recall curve. Numbers are on Wider Face validation subset.

Inputs

Image, name: input, shape: 1, 3, 384, 672 in the format B, C, H, W, where:

  • B - batch size

  • C - number of channels

  • H - image height

  • W - image width

Expected color order is 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. The results are sorted by confidence in decreasing order. 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

Legal Information

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