• Face Detection
• • Introduction
  • Results
  • • WiderFace Val Performance in multiscale.
    • WiderFace Val Performance in single scale When using MobileNet0.25 as backbone.
  • Quick Start
  • Demo
  • RetinaFace Pretrained Models
  • References

Face Detection

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Introduction

MindFace is an open source toolkit based on MindSpore and currently contains state-of-the-art face recognition and detection models such as ArcFace, RetinaFace, etc. MindFace has a unified application programming interface and powerful scalability for common application scenarios such as facial recognition and detection.

This is a face detection repository for the MindFace. The deep learning models currently available for users are RetinaFace network models based on different backbone networks (resnet50 and mobiletnet0.25). RetinaFace is a single-stage SOTA face detector which is accepted by CVPR 2020. The ResNet50-based version we implemented provides better performance, while the MobileNet0.25-based version is faster to detect.

Results

WiderFace Val Performance in multiscale.

backbone	Easy	Medium	Hard
mobileNet0.25	91.60%	89.50%	82.39%

WiderFace Val Performance in single scale When using MobileNet0.25 as backbone.

Style	Easy	Medium	Hard
MindSpore (same parameter with MXNet)	88.51%	86.86%	80.88%
MindSpore (original image scale)	90.77%	88.20%	74.76%
PyTorch (same parameter with MXNet)	88.67%	87.09%	80.99%
PyTorch (original image scale)	90.70%	88.16%	73.82%
MXNet	88.72%	86.97%	79.19%
MXNet(original image scale)	89.58%	87.11%	69.12%

Quick Start

1. Installation

   1.1 Git clone this repo and install mindface.

   git clone https://github.com/mindspore-lab/mindface.git
   cd mindface
   python setup.py install
   

   1.2 Install dependencies

   cd mindface/detection/
   pip install -r requirements.txt
   

2. Prepare Data

   2.1. Download WiderFace dataset and annotations that we used from baidu cloud or google drive.

   2.2. Organise the dataset directory under mindface/detection/ as follows:

   data/WiderFace/
       train/
           images/
           label.txt
       val/
           images/
           label.txt
       ground_truth/
           wider_easy_val.mat
           wider_medium_val.mat
           wider_hard_val.mat
           wider_face_val.mat
   

3. Set Config File

   You can Modify the parameters of the config file in ./configs.

   We provide config file (RetinaFace_mobilenet025.

4. Training

   It is easy to train your model using train.py, where the training strategy (e.g., augmentation, LR scheduling) can be configured with external arguments or a yaml config file.

   • Standalone Training

       python mindface/detection/train.py --config mindface/detection/configs/RetinaFace_mobilenet025.yaml
   

   • Distributed Training

     To run in distributed mode, openmpi is required to install.

       export CUDA_VISIBLE_DEVICES=0,1,2,3  # 4 GPUs
       mpirun -n 4 python mindface/detection/train.py --config mindface/detection/configs/RetinaFace_mobilenet025.yaml
   

   note: if your device is Ascend, please set the "device_target" in the config file to "Ascend".

5. Validation

   To evalute the model performance, please run eval.py

    python eval.py --config mindface/detection/configs/RetinaFace_mobilenet025.yaml --checkpoint pretrained/weight.ckpt

6. Inference

   To infer a single image, please run infer.py

    python infer.py --config mindface/detection/configs/RetinaFace_mobilenet025.yaml --checkpoint pretrained/weight.ckpt --image_path ./imgs/0000.jpg --conf 0.5

Demo

The inference results by using our pre-trained weights are shown in the following figure.

RetinaFace Pretrained Models

You can download our pretrained model
RetinaFace-MobileNet025.

You can verify the results in the table with the downloaded pretrained model.

References

• Retinaface (MXNet)
• Retinaface (PyTorch)

@inproceedings{deng2019retinaface,
title={RetinaFace: Single-stage Dense Face Localisation in the Wild},
author={Deng, Jiankang and Guo, Jia and Yuxiang, Zhou and Jinke Yu and Irene Kotsia and Zafeiriou, Stefanos},
booktitle={arxiv},
year={2019}