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 Inference Engine Format

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.

An example of using the Model Downloader:

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>