YOLO-FaceV2: A Scale and Occlusion Aware Face Detector
Pattern Recognition or arxiv
Create a Python Virtual Environment.
conda create -n {name} python=x.xEnter 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.htmlInstall other python package.
pip install -r requirements.txtbash data/scripts/get_widerface.shDownload 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.pycd data
python3 train2yolo.py /path/to/original/widerface/train [/path/to/save/widerface/train]
python3 val2yolo.py /path/to/original/widerface [/path/to/save/widerface/val]| Model | Easy | Medium | Hard | PreWeight |
|---|---|---|---|---|
| YOLO-Facev2n | 0.936 | 0.921 | 0.812 | yolo-facev2n.pt |
| YOLO-FaceV2s | 0.983 | 0.970 | 0.893 | yolo-facev2s.pt |
| YOLO-FaceV2m | 0.984 | 0.973 | 0.905 | yolo-facev2m.pt |
| YOLO-FaceV2l | 0.986 | 0.979 | 0.919 | yolo-facev2l.pt |
python train.py --weights preweight.pt
--data data/WIDER_FACE.yaml
--cfg models/yolov5s_v2_RFEM_MultiSEAM.yaml
--batch-size 32
--epochs 250python3 test_widerface.py --weights 'your test model' --img-size 640
cd widerface_evaluate/
python evaluation.py --pred ./widerface_txt_xVisualization of small-scale face detection effect:
Visualization of the heat map effect of attention covering human faces:
see in ultralytics/yolov5#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)" &
donehttps://github.com/ultralytics/yolov5
https://github.com/deepcam-cn/yolov5-face
https://github.com/open-mmlab/mmdetection
https://github.com/dongdonghy/repulsion_loss_pytorch
If you think this work is helpful for you, please cite
@article{YU2024110714,
title = {YOLO-FaceV2: A scale and occlusion aware face detector},
journal = {Pattern Recognition},
pages = {110714},
year = {2024},
issn = {0031-3203},
url = {https://www.sciencedirect.com/science/article/pii/S0031320324004655},
author = {Ziping Yu and Hongbo Huang and Weijun Chen and Yongxin Su and Yahui Liu and Xiuying Wang},
keywords = {Face detection, YOLO, Scale-aware, Occlusion, Imbalance problem},
}
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