# faceboxes-pytorch¶

## Use Case and High-Level Description¶

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

Metric

Value

Type

Object detection

GFLOPs

1.8975

MParams

1.0059

Source framework

PyTorch*

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.

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>

## Use Case and High-Level Description¶

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

Metric

Value

Type

Object detection

GFLOPs

1.8975

MParams

1.0059

Source framework

PyTorch*

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.

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>

## Legal Information¶

MIT License

Copyright (c) 2017 Max deGroot, Ellis Brown
Copyright (c) 2019 Zisian Wong, Shifeng Zhang

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.