# landmarks-regression-retail-0009¶

## Use Case and High-Level Description¶

This is a lightweight landmarks regressor for the Smart Classroom scenario. It has a classic convolutional design: stacked 3x3 convolutions, batch normalizations, PReLU activations, and poolings. Final regression is done by the global depthwise pooling head and FullyConnected layers. The model predicts five facial landmarks: two eyes, nose, and two lip corners.

## Example¶

## Specification¶

Metric |
Value |
---|---|

Mean Normed Error (on VGGFace2) |
0.0705 |

Face location requirements |
Tight crop |

GFlops |
0.021 |

MParams |
0.191 |

Source framework |
PyTorch* |

Normed Error (NE) for i^{th} sample has the following form:

where N is the number of landmarks, *p*-hat and *p* are, correspondingly, the prediction and ground truth vectors of k^{th} landmark of i^{th} sample, and d_{i} is the interocular distance for i^{th} sample.

## Inputs¶

Image, name: `0`

, shape: `1, 3, 48, 48`

in the format `B, C, H, W`

, where:

`B`

- batch size`C`

- number of channels`H`

- image height`W`

- image width

The expected color order is `BGR`

.

## Outputs¶

The net outputs a blob with the shape: `1, 10, 1, 1`

, containing a row-vector of 10 floating point values
for five landmarks coordinates in the form (x0, y0, x1, y1, …, x4, y4).
All the coordinates are normalized to be in range [0, 1].

## Demo usage¶

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

## Legal Information¶

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