# face-detection-retail-0044¶

## Use Case and High-Level Description¶

Face detector based on SqueezeNet light (half-channels) as a backbone with a single SSD for indoor/outdoor scenes shot by a front-facing camera. The backbone consists of fire modules to reduce the number of computations. The single SSD head from 1/16 scale feature map has nine clustered prior boxes.

## Specification¶

Metric

Value

AP ( WIDER )

83.00%

GFlops

1.067

MParams

0.588

Source framework

Caffe*

Average Precision (AP) is defined as an area under the precision/recall curve. All numbers were evaluated by taking into account only faces bigger than 60 x 60 pixels.

## Inputs¶

### Original Model¶

Image, name: input, shape: 1, 3, 300, 300, format B, C, H, W, where:

• B - batch size

• C - number of channels

• H - image height

• W - image width

Expected color order: BGR.

### Converted Model¶

Image, name: input, shape: 1, 3, 300, 300, format B, C, H, W, where:

• B - batch size

• C - number of channels

• H - image height

• W - image width

Expected color order: BGR.

## Outputs¶

### Original Model¶

The net outputs a blob with shape: 1, 1, 200, 7 in the format 1, 1, N, 7, where N is the number of detected bounding boxes. For each detection, the description has the format: [image_id, label, conf, x_min, y_min, x_max, y_max], where:

• image_id - ID of the image in the batch

• label - predicted class ID

• conf - confidence for the predicted class

• (x_min, y_min) - coordinates of the top left bounding box corner

• (x_max, y_max) - coordinates of the bottom right bounding box corner.

### Converted Model¶

The net outputs a blob with shape: 1, 1, 200, 7 in the format 1, 1, N, 7, where N is the number of detected bounding boxes. For each detection, the description has the format: [image_id, label, conf, x_min, y_min, x_max, y_max], where:

• image_id - ID of the image in the batch

• label - predicted class ID

• conf - confidence for the predicted class

• (x_min, y_min) - coordinates of the top left bounding box corner

• (x_max, y_max) - coordinates of the bottom right bounding box corner.

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

omz_downloader --name <model_name>
omz_converter --name <model_name>