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Face Detection Model

Introduction

face_detection High efficiency, high speed face detection solutions, including the most advanced models and classic models.

Model Library

A mAP on the WIDERFACE dataset

Network structure size images/GPUs Learning rate strategy Easy/Medium/Hard Set Prediction delay（SD855） Model size(MB) Download Configuration File
BlazeFace 640 8 1000e 0.885 / 0.855 / 0.731 - 0.472 link Configuration File
BlazeFace-FPN-SSH 640 8 1000e 0.907 / 0.883 / 0.793 - 0.479 link Configuration File

Attention:

Quick Start

Data preparation

We use WIDER-FACE dataset for training and model tests, the official web site provides detailed data is introduced.

  • WIDER-Face data source:

  • Load a dataset of type wider_face using the following directory structure:

    dataset/wider_face/
├── wider_face_split
│   ├── wider_face_train_bbx_gt.txt
│   ├── wider_face_val_bbx_gt.txt
├── WIDER_train
│   ├── images
│   │   ├── 0--Parade
│   │   │   ├── 0_Parade_marchingband_1_100.jpg
│   │   │   ├── 0_Parade_marchingband_1_381.jpg
│   │   │   │   ...
│   │   ├── 10--People_Marching
│   │   │   ...
├── WIDER_val
│   ├── images
│   │   ├── 0--Parade
│   │   │   ├── 0_Parade_marchingband_1_1004.jpg
│   │   │   ├── 0_Parade_marchingband_1_1045.jpg
│   │   │   │   ...
│   │   ├── 10--People_Marching
│   │   │   ...

  • Manually download the dataset: To download the WIDER-FACE dataset, run the following command:

cd dataset/wider_face && ./download_wider_face.sh

Parameter configuration

The configuration of the base model can be referenced to configs/face_detection/_base_/blazeface.yml； Improved model to add FPN and SSH neck structure, configuration files can be referenced to configs/face_detection/_base_/blazeface_fpn.yml, You can configure FPN and SSH as required

BlazeNet:
   blaze_filters: [[24, 24], [24, 24], [24, 48, 2], [48, 48], [48, 48]]
   double_blaze_filters: [[48, 24, 96, 2], [96, 24, 96], [96, 24, 96],
                           [96, 24, 96, 2], [96, 24, 96], [96, 24, 96]]
   act: hard_swish #Configure Blaze Block activation function in Backbone. The basic model is Relu. hard_swish is needed to add FPN and SSH

BlazeNeck:
   neck_type : fpn_ssh #only_fpn, only_ssh and fpn_ssh
   in_channel: [96,96]

Training and Evaluation

The training process and evaluation process methods are consistent with other algorithms, please refer to GETTING_STARTED_cn.md。
Attention: Face detection models currently do not support training and evaluation.

Evaluated on the WIDER-FACE Dataset

  • Step 1: Evaluate and generate a result file:
python -u tools/eval.py -c configs/face_detection/blazeface_1000e.yml \
       -o weights=output/blazeface_1000e/model_final \
       multi_scale=True

Set multi_scale=True for multi-scale evaluation. After evaluation, test results in TXT format will be generated in output/pred.

  • Step 2: Download the official evaluation script and Ground Truth file:
wget http://mmlab.ie.cuhk.edu.hk/projects/WIDERFace/support/eval_script/eval_tools.zip
unzip eval_tools.zip && rm -f eval_tools.zip
  • Step 3: Start the evaluation

Method 1: Python evaluation:

git clone https://github.com/wondervictor/WiderFace-Evaluation.git
cd WiderFace-Evaluation
# compile
python3 setup.py build_ext --inplace
# Begin to assess
python3 evaluation.py -p /path/to/PaddleDetection/output/pred -g /path/to/eval_tools/ground_truth

Method 2: MatLab evaluation:

# Change the name of save result path and draw curve in `eval_tools/wider_eval.m`:
pred_dir = './pred';  
legend_name = 'Paddle-BlazeFace';

`wider_eval.m` is the main implementation of the evaluation module. Run the following command:
matlab -nodesktop -nosplash -nojvm -r "run wider_eval.m;quit;"

Citations

@article{bazarevsky2019blazeface,
      title={BlazeFace: Sub-millisecond Neural Face Detection on Mobile GPUs},
      author={Valentin Bazarevsky and Yury Kartynnik and Andrey Vakunov and Karthik Raveendran and Matthias Grundmann},
      year={2019},
      eprint={1907.05047},
      archivePrefix={arXiv},