Use Case and High-Level Description

This is a vehicle detection network based on an SSD framework with tuned MobileNet v1 as a feature extractor and using binary layer for speedup. This detecector was created by binarization the vehicle-detection-adas-0002




Metric Value
Average Precision (AP) 89.2%
Target vehicle size 40 x 30 pixels on Full HD image
Max objects to detect 200
GFlops 0.75
GI1ops 2.048
MParams 1.079
Source framework PyTorch*

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

Tested on a challenging internal dataset with 3000 images and 12585 vehicles 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 the 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.

The binary network was tuned from vehicle-detection-adas-0002 model