This is a MobileNetV2 + SSD-based vehicle and (Chinese) license plate detector for the "Barrier" use case.
|Mean Average Precision (mAP)||99.65%|
|Car pose||Front facing cars|
|Min plate width||96 pixels|
|Max objects to detect||200|
Average Precision (AP) is defined as an area under the precision/recall curve. Validation dataset is BIT-Vehicle.
input, shape: [1x3x300x300] - An input image in the format [BxCxHxW], where:
Expected color order is BGR.
The net outputs blob with shape: [1, 1, N, 7], where N is the number of detected bounding boxes. Each detection has the format [
image_id- ID of the image in the batch
label- predicted class ID
conf- confidence for the predicted class
y_min) - coordinates of the top left bounding box corner
y_max) - coordinates of the bottom right bounding box corner.
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