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 (Datatang) | 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.
Link to performance table
-
name: "input" , shape: [1x3x320x544] - An input image in the format [BxCxHxW], where:
- B - batch size
- C - number of channels
- H - image height
- W - image width
Expected color order is BGR.
- The net outputs blob with shape: [1, 1, N, 7], where N is the number of detected
bounding boxes. For each detection, the description has the format:
[
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 class- (
x_min,y_min) - coordinates of the top left bounding box corner - (
x_max,y_max) - coordinates of the bottom right bounding box corner.
[*] Other names and brands may be claimed as the property of others.