This is a pedestrian detector for the Retail scenario. It is based on MobileNetV2-like backbone that includes depth-wise convolutions to reduce the amount of computation for the 3x3 convolution block. The single SSD head from 1/16 scale feature map has 12 clustered prior boxes.
Metric | Value |
---|---|
AP | 88.62% |
Pose coverage | Standing upright, parallel to image plane |
Support of occluded pedestrians | YES |
Occlusion coverage | <50% |
Min pedestrian height | 100 pixels (on 1080p) |
GFlops | 2.300 |
MParams | 0.723 |
Source framework | Caffe* |
Average Precision (AP) is defined as an area under the precision/recall curve.
Name: input
, shape: [1x3x320x544] - 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 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.