Use Case and High-level Description

This is a MobileNetV2 + SSD-based vehicle and (Chinese) license plate detector for the "Barrier" use case.




Metric Value
Mean Average Precision (mAP) 98.62%
AP vehicles 98.03%
AP plates 99.21%
Car pose Front facing cars
Min plate width 96 pixels
Max objects to detect 200
GFlops 0.349
MParams 0.634
Source framework TensorFlow*

Average Precision (AP) is defined as an area under the precision/recall curve. Validation dataset is BIT-Vehicle.



  1. name: "input" , shape: [1x3x300x300] - 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

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