Use Case and High-Level Description¶
This is a person, vehicle, bike detector that is based on MobileNetV2 backbone with two SSD heads from 1/16 and 1/8 scale feature maps and clustered prior boxes for 384x384 resolution.
AP @ [ IoU=0.50:0.95 ]
0.2259 (internal test set)
Average Precision (AP) is defined as an area under the precision/recall curve.
1, 3, 384, 384 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 (0 - vehicle, 1 - person, 2 - bike)
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
The OpenVINO Training Extensions provide a training pipeline, allowing to fine-tune the model on custom dataset.
The model can be used in the following demos provided by the Open Model Zoo to show its capabilities:
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