# ultra-lightweight-face-detection-slim-320¶

## Use Case and High-Level Description¶

Ultra-lightweight Face Detection slim 320 is a version of the lightweight face detection model with network backbone simplification. The model designed for edge computing devices and pre-trained on the WIDER FACE dataset with 320x240 input resolutions.

For details see repository.

Metric

Value

Type

Object detection

GFLOPs

0.1724

MParams

0.2844

Source framework

PyTorch*

Metric

Value

mAP

83.32%

## Input¶

### Original model¶

Image, name - input, shape - 1, 3, 240, 320, format B, C, H, W, where:

• B - batch size

• C - number of channels

• H - image height

• W - image width

Expected color order is RGB.

Mean values - [127.0, 127.0, 127.0]. Scale values - [128.0, 128.0, 128.0].

### Converted model¶

Image, name - input, shape - 1, 3, 240, 320, format B, C, H, W, where:

• B - batch size

• C - number of channels

• H - image height

• W - image width

Expected color order is BGR.

## Output¶

### Original model¶

1. Bounding boxes, name: boxes, shape - 1, 4420, 4. Presented in format B, A, 4, where:

• B - batch size

• A - number of detected anchors

For each detection, the description has the format: [x_min, y_min, x_max, y_max], where:

• (x_min, y_min) - coordinates of the top left bounding box corner (coordinates are in normalized format, in range [0, 1])

• (x_max, y_max) - coordinates of the bottom right bounding box corner (coordinates are in normalized format, in range [0, 1])

2. Scores, name: scores, shape - 1, 4420, 2. Contains scores for 2 classes - the first is background, the second is face.

### Converted model¶

1. Bounding boxes, name: boxes, shape - 1, 4420, 4. Presented in format B, A, 4, where:

• B - batch size

• A - number of detected anchors

For each detection, the description has the format: [x_min, y_min, x_max, y_max], where:

• (x_min, y_min) - coordinates of the top left bounding box corner (coordinates are in normalized format, in range [0, 1])

• (x_max, y_max) - coordinates of the bottom right bounding box corner (coordinates are in normalized format, in range [0, 1])

2. Scores, name: scores, shape - 1, 4420, 2. Contains scores for 2 classes - the first is background, the second is face.

You can download models and if necessary convert them into Inference Engine format using the Model Downloader and other automation tools as shown in the examples below.

python3 <omz_dir>/tools/downloader/downloader.py --name <model_name>

An example of using the Model Converter:

python3 <omz_dir>/tools/downloader/converter.py --name <model_name>