Use Case and High-level Description
This is a MobileNetV2 + SSD-based vehicle and (Chinese) license plate detector for the "Barrier" use case.
Example
Specification
| 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.
Performance
Link to performance table
Inputs
-
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.
Outputs
- 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. [*] Other names and brands may be claimed as the property of others.