Use Case and High-Level Description

Pedestrian detection network based on SSD framework with tuned MobileNet v1 as a feature extractor.




Metric Value
Average Precision (AP) 88%
Target pedestrian size 60 x 120 pixels on Full HD image
Max objects to detect 200
GFlops 2.836
MParams 1.165
Source framework Caffe*

Average Precision metric described in: Mark Everingham et al. The PASCAL Visual Object Classes (VOC) Challenge.

Tested on an internal dataset with 1001 pedestrian to detect.



  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 a 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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