Pedestrian and vehicle detection network based on MobileNet v1.0 + SSD.
|AP for pedestrians||88%|
|AP for vehicles||90%|
|Target pedestrian size||60x120 pixels|
|Target vehicle size||40x30 pixels|
Average Precision (AP) metric is described in: Mark Everingham et al. The PASCAL Visual Object Classes (VOC) Challenge.
Tested on challenging internal datasets with 1001 pedestrian and 12585 vehicles to detect.
input, shape: [1x3x384x672] - An input image in the format [BxCxHxW], where:
The net outputs blob with shape: [1, 1, N, 7], where N is the number of detected bounding boxes. Each detection has the format [
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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