# facial-landmarks-98-detection-0001¶

## Use Case and High-Level Description¶

This is a 2D facial landmarks detection network based on the HRNet approach. For face in an image, the network detects landmarks (look at image below). The landmarks contain 98 keypoints.

## Dataset (training and validation) - Internal¶

Network is trained and validated on the custom dataset based on WiderFace and VGG2 subsets.

Metric

Value

NME

0.1323

GFlops

0.6

MParams

9.66

Source framework

PyTorch*

## Inputs¶

Name: input.1, shape: 1, 3, 64, 64. An input image in the B, C, H, W format, where:

• B - batch size

• C - number of channels

• H - image height

• W - image width Expected color order is BGR.

## Outputs¶

The net outputs a blob 3851 with the shape: 1, 98, 16, 16, containing location heatmaps for 98 keypoints. Locations that are filtered out by non-maximum suppression algorithm have negated values assigned to them.

## Demo usage¶

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