Face detector for driver monitoring and similar scenarios. The network features a pruned MobileNet backbone that includes depth-wise convolutions to reduce the amount of computation for the 3x3 convolution block. Also some 1x1 convolutions are binary that can be implemented using effective binary XNOR+POPCOUNT approach
|AP (head height >10px)||31.2%|
|AP (head height >32px)||76.2%|
|AP (head height >64px)||90.3%|
|AP (head height >100px)||91.9%|
|Min head size||90x90 pixels on 1080p|
name: "input" , shape: [1x3x384x672] - An input image in the format [BxCxHxW], where:
Expected color order is BGR.
image_id- ID of the image in the batch
label- predicted class ID
conf- confidence for the predicted class
y_min) - coordinates of the top left bounding box corner
y_max) - coordinates of the bottom right bounding box corner.
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The NET was tuned from face-detection-adas-0001 weights