This is a vehicle detection network based on an SSD framework with tuned MobileNet v1 as a feature extractor.
Metric | Value |
---|---|
Average Precision (AP) | 90.6% |
Target vehicle size | 40 x 30 pixels on Full HD image |
Max objects to detect | 200 |
GFlops | 2.798 |
MParams | 1.079 |
Source framework | Caffe* |
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.
Link to performance table
image_id
, label
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]image_id
- ID of the image in the batchlabel
- predicted class IDconf
- confidence for the predicted classx_min
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) - coordinates of the top left bounding box cornerx_max
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) - coordinates of the bottom right bounding box corner.[*] Other names and brands may be claimed as the property of others.