Awesome
YOLO-FaceV2
Introduction
YOLO-FaceV2: A Scale and Occlusion Aware Face Detector
https://arxiv.org/abs/2208.02019
Framework Structure
Environment Requirments
Create a Python Virtual Environment.
conda create -n {name} python=x.x
Enter Python Virtual Environment.
conda activate {name}
Install pytorch in this.
pip install torch==1.10.0+cu111 torchvision==0.11.0+cu111 torchaudio==0.10.0 -f https://download.pytorch.org/whl/torch_stable.html
Install other python package.
pip install -r requirements.txt
Step-Through Example
Installation
Get the code.
git clone https://github.com/Krasjet-Yu/YOLO-FaceV2.git
Dataset
Download the WIDER FACE dataset. Then convert it to YOLO format.
# You can modify convert.py and voc_label.py if needed.
python3 data/convert.py
python3 data/voc_label.py
Preweight
The link is yolo-facev2s.pt
Training
Train your model on WIDER FACE.
python train.py --weights preweight.pt
--data data/WIDER_FACE.yaml
--cfg models/yolov5s_v2_RFEM_MultiSEAM.yaml
--batch-size 32
--epochs 250
Test
python detect.py --weights ./preweight/best.pt --source ./data/images/test.jpg --plot-label --view-img
Evaluate
Evaluate the trained model via next code on WIDER FACE
If you don't want to train, you can also directly use our trained model to evaluate.
The link is yolo-facev2_last.pt
python widerface_pred.py --weights runs/train/x/weights/best.pt
--save_folder ./widerface_evaluate/widerface_txt_x
cd widerface_evaluate/
python evaluation.py --pred ./widerface_txt_x
Download the eval_tool to show the performance.
The result is shown below:
Finetune
see in https://github.com/ultralytics/yolov5/issues/607
# Single-GPU
python train.py --epochs 10 --data coco128.yaml --weights yolov5s.pt --cache --evolve
# Multi-GPU
for i in 0 1 2 3 4 5 6 7; do
sleep $(expr 30 \* $i) && # 30-second delay (optional)
echo 'Starting GPU '$i'...' &&
nohup python train.py --epochs 10 --data coco128.yaml --weights yolov5s.pt --cache --device $i --evolve > evolve_gpu_$i.log &
done
# Multi-GPU bash-while (not recommended)
for i in 0 1 2 3 4 5 6 7; do
sleep $(expr 30 \* $i) && # 30-second delay (optional)
echo 'Starting GPU '$i'...' &&
"$(while true; do nohup python train.py... --device $i --evolve 1 > evolve_gpu_$i.log; done)" &
done
Reference
https://github.com/ultralytics/yolov5
https://github.com/deepcam-cn/yolov5-face
https://github.com/open-mmlab/mmdetection
https://github.com/dongdonghy/repulsion_loss_pytorch
Cite
If you think this work is helpful for you, please cite
@ARTICLE{2022arXiv220802019Y,
author = {{Yu}, Ziping and {Huang}, Hongbo and {Chen}, Weijun and {Su}, Yongxin and {Liu}, Yahui and {Wang}, Xiuying},
title = "{YOLO-FaceV2: A Scale and Occlusion Aware Face Detector}",
journal = {arXiv e-prints},
keywords = {Computer Science - Computer Vision and Pattern Recognition},
year = 2022,
month = aug,
eid = {arXiv:2208.02019},
pages = {arXiv:2208.02019},
archivePrefix = {arXiv},
eprint = {2208.02019},
primaryClass = {cs.CV},
adsurl = {https://ui.adsabs.harvard.edu/abs/2022arXiv220802019Y},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
Contact
We use code's license is MIT License. The code can be used for business inquiries or professional support requests.