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.
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.