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.




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.


Link to performance table


  1. name: "input" , shape: [1x3x384x672] - An input image in the format [BxCxHxW], where:

    • B - batch size
    • C - number of channels
    • H - image height
    • W - image width

    Expected color order is BGR.


  1. The net outputs 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]
    • 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.

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