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.
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.