This is a MobileNetV2 + SSD-based vehicle and (Chinese) license plate detector for the "Barrier" use case.
Metric | Value |
---|---|
Mean Average Precision (mAP) | 99.65% |
AP vehicles | 99.88% |
AP plates | 99.42% |
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.
name: "input" , shape: [1x3x300x300] - An input image in the format [BxCxHxW], where:
Expected color order is BGR.
image_id
, label
, conf
, x_min
, y_min
, x_max
, y_max
]image_id
- ID of the image in the batchlabel
- predicted class IDconf
- confidence for the predicted classx_min
, y_min
) - coordinates of the top left bounding box cornerx_max
, y_max
) - coordinates of the bottom right bounding box corner.[*] Other names and brands may be claimed as the property of others.