faceboxes-pytorch

Use Case and High-Level Description

FaceBoxes: A CPU Real-time Face Detector with High Accuracy. For details see the repository, paper

Specification

Metric

Value

Type

Object detection

GFLOPs

1.8975

MParams

1.0059

Source framework

PyTorch*

Accuracy

Metric

Value

mAP

83.565%

Input

Original model

Image, name - input.1, shape - 1, 3, 1024, 1024, format - B, C, H, W, where:

  • B - batch size

  • C - number of channels

  • H - image height

  • W - image width

Expected color order - BGR. Mean values - [104.0, 117.0, 123.0]

Converted model

Image, name - input.1, shape - 1, 3, 1024, 1024, format - B, C, H, W, where:

  • B - batch size

  • C - number of channels

  • H - image height

  • W - image width

Expected color order - BGR.

Output

Original model

  1. Bounding boxes deltas, name: boxes, shape - 1, 21824, 4. Presented in format B, A, 4, where:

    • B - batch size

    • A - number of prior box anchors

  2. Scores, name: scores, shape - 1, 21824, 2. Contains scores for 2 classes - the first is background, the second is face.

Converted model

The converted model has the same parameters as the original model.

Download a Model and Convert it into OpenVINO™ IR Format

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.

An example of using the Model Downloader:

omz_downloader --name <model_name>

An example of using the Model Converter:

omz_converter --name <model_name>

Demo usage

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