This is a vehicle 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 256x256 resolution.
Metric | Value |
---|---|
AP @ [ IoU=0.50:0.95 ] | 0.254 (internal test set) |
GFlops | 0.786 |
MParams | 1.817 |
Source framework | PyTorch* |
Average Precision (AP) is defined as an area under the precision/recall curve.
Name: input
, shape: [1x3x256x256] - An input image in the format [BxCxHxW], where:
Expected color order is BGR.
The net outputs blob with shape: [1, 1, N, 7], where N is the number of detected bounding boxes. Each detection has the format [image_id
, label
, conf
, x_min
, y_min
, x_max
, y_max
], where:
image_id
- ID of the image in the batchlabel
- predicted class ID (0 - vehicle)conf
- 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.