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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

Image, name: image, shape: 1, 3, 800, 1344 in the format B, C, H, W, where:

  • B - batch size

  • C - number of channels

  • H - image height

  • W - image width

Expected color order is BGR.

Outputs

Model has outputs with dynamic shapes.

  1. The boxes is a blob with the shape -1, 5 in the format 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 -1 in the format N, where N is the number of detected bounding boxes. It contains predicted class ID (0 - person) per each detected box.

Demo usage

The model can be used in the following demos provided by the Open Model Zoo to show its capabilities:

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