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.
1, 3, 384, 672 in the format
B, C, H, W, where:
B- batch size
C- number of channels
H- image height
W- image width
Expected color order is
The net outputs blob with shape:
1, 1, 200, 7 in the format
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 (1 - vehicle)
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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