This is a vehicle detection network based on an SSD framework with tuned MobileNet v1 as a feature extractor and using binary layer for speedup. This detecector was created by binarization the vehicle-detection-adas-0002
Metric | Value |
---|---|
Average Precision (AP) | 89.2% |
Target vehicle size | 40 x 30 pixels on Full HD image |
Max objects to detect | 200 |
GFlops | 0.75 |
GI1ops | 2.048 |
MParams | 1.079 |
Source framework | Pytorch* |
Average Precision metric described in: Mark Everingham et al. "The PASCAL Visual Object Classes (VOC) Challenge".
Tested on a challenging internal dataset with 3000 images and 12585 vehicles to detect.
image_id
, label
, conf
, x_min
, y_min
, x_max
, y_max
]image_id
- ID of the image in the batchlabel
- predicted class IDconf
- confidence for the predicted classx_min
, y_min
) - coordinates of the top left bounding box cornerx_max
, y_max
) - coordinates of the bottom right bounding box corner.[*] Other names and brands may be claimed as the property of others.
The binary network was tuned from vehicle-detection-adas-0002 model