# person-detection-asl-0001¶

## Use Case and High-Level Description¶

This is a person detector for the ASL Recognition scenario. It is based on ShuffleNetV2-like backbone that includes depth-wise convolutions to reduce the amount of computation for the 3x3 convolution block and FCOS head.

## Specification¶

Metric

Value

Persons AP on COCO

79.35%

Minimal person height

100 pixel

GFlops

0.986

MParams

1.338

Source framework

PyTorch*

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

## Inputs¶

Image, name: image, shape: 1, 3, 320, 320 in the format 1, C, H, W, where:

• C - number of channels

• H - image height

• W - image width

Expected color order is BGR.

## Outputs¶

The net outputs blob with shape: 100, 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

## Demo usage¶

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