Use case and High-level description

This is a pedestrian detector for the Retail scenario. It is based on MobileNetV2-like backbone that includes depth-wise convolutions to reduce the amount of computation for the 3x3 convolution block. The single SSD head from 1/16 scale feature map has 12 clustered prior boxes.




Metric Value
AP 88.62%
Pose coverage Standing upright, parallel to image plane
Support of occluded pedestrians YES
Occlusion coverage <50%
Min pedestrian height 100 pixels (on 1080p)
GFlops 2.300
MParams 0.723
Source framework Caffe*

Average Precision (AP) is defined as an area under the precision/recall curve.



  1. name: "input" , shape: [1x3x320x544] - 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.

Legal Information

[*] Other names and brands may be claimed as the property of others.