Use Case and High-Level Description
This is a person detector that is based on Cascade R-CNN architecture with ResNet50 backbone.
Example
Specification
| Metric | Value |
|---|---|
| AP | 94.54% (internal test set) |
| Pose coverage | Standing upright, parallel to image plane |
| Support of occluded persons | YES |
| Occlusion coverage | <50% |
| Min person height | 100 pixels (on 1080p) |
| GFlops | 404.264 |
| MParams | 71.565 |
| Source framework | PyTorch* |
Average Precision (AP) is defined as an area under the precision/recall curve. Intersection over union threshold of 0.5 is used for matching.
Performance
Inputs
Name: input, shape: [1x3x800x1344] - 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.
Outputs
- The
boxesis a blob with the shape [N, 5], where N is the number of detected bounding boxes. For each detection, the description has the format [x_min,y_min,x_max,y_max,conf], where:- (
x_min,y_min) - coordinates of the top left bounding box corner - (
x_max,y_max) - coordinates of the bottom right bounding box corner conf- confidence for the predicted class
- (
- The
labelsis a blob with the shape [N], where N is the number of detected bounding boxes. It containslabelper each detected box.
Legal Information
[*] Other names and brands may be claimed as the property of others.