README_en.md 5.1 KB
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:
- We use a multi-scale evaluation strategy to get the mAP in
Easy/Medium/Hard Set. Please refer to the evaluation on the WIDER FACE dataset for details.
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_faceusing 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=TrueSet
multi_scale=Truefor multi-scale evaluation. After evaluation, test results in TXT format will be generated inoutput/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},
```