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.
Link to performance table
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. [*] Other names and brands may be claimed as the property of others.