This is a vehicle detection network based on an SSD framework with tuned MobileNet v1 as a feature extractor.
|Average Precision (AP)||90.6%|
|Target vehicle size||40 x 30 pixels on Full HD image|
|Max objects to detect||200|
For Average Precision metric description, see The PASCAL Visual Object Classes (VOC) Challenge.
Tested on a challenging internal dataset with 3000 images and 12585 vehicles to detect.
input, shape: [1x3x384x672] - An input image in the format [BxCxHxW], where:
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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