Use Case and High-Level Description

Pedestrian detection network based on SSD framework with tuned MobileNet v1 as a feature extractor. Some layers of MobileNet v1 are binary and use I1 arithm




Metric Value
Average Precision (AP) 84%
Target pedestrian size 60 x 120 pixels on Full HD image
Max objects to detect 200
GFlops 0.750
GI1ops 2.086
MParams 1.165
Source framework PyTorch*

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.



Name: 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, label, conf, x_min, y_min, x_max, y_max], where:

Legal Information

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The net is tuned from pedestrian-detection-adas-0002.