person-detection-0106.md

person-detection-0106

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 @ [ IoU=0.50:0.95 ]	0.442 (internal test set)
GFlops	404.264
MParams	71.565
Source framework	PyTorch*

Average Precision (AP) is defined as an area under the precision/recall curve.

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

1. The boxes is 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
2. The labels is a blob with the shape [N], where N is the number of detected bounding boxes. It contains label (0 - person) per each detected box.

Legal Information

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