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