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 Inference Engine format using the Model Downloader and other automation tools as shown in the examples below.