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.

Example

_images/person-detection-asl-0001.png

Specification

Metric

Value

Persons AP on COCO

80.0%

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: input, 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

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.

Example

_images/person-detection-asl-0001.png

Specification

Metric

Value

Persons AP on COCO

80.0%

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: input, 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

Legal Information

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