This is a pedestrian detector based on backbone with hyper-feature + R-FCN for the Retail scenario.
| Metric | Value |
|---|---|
| AP | 80.14% |
| Pose coverage | Standing upright, parallel to image plane |
| Support of occluded pedestrians | YES |
| Occlusion coverage | <50% |
| Min pedestrian height | 80 pixels (on 1080p) |
| Max objects to detect | 200 |
| GFlops | 12.427 |
| MParams | 3.244 |
| Source framework | Caffe* |
Average Precision (AP) is defined as an area under the precision/recall curve. Validation dataset consists of ~50K of images from ~100 different scenes.
data , shape: [1x3x544x992] - An input image in following format [1xCxHxW]. The expected channel order is BGR.im_info, shape: [1x6] - An image information [544, 992, 992/frame_width, 544/frame_height, 992/frame_width, 544/frame_height]image_id, label, conf, x_min, y_min, x_max, y_max], where:image_id - ID of image in batchlabel - ID of predicted classconf - 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.