YOLOv8-Face

The YOLOv8-Face repository provides pre-trained models designed specifically for face detection. The models have been pre-trained by Lindevs from scratch.

Release Notes

• [2024-11-01] Re-saved and re-uploaded PyTorch models to avoid the dill package usage warning.
• [2023-12-09] YOLOv8x-Face model has been added.
• [2023-12-02] YOLOv8n-Face, YOLOv8s-Face, YOLOv8m-Face and YOLOv8l-Face models has been added.

Pre-trained Models

The models have been trained on WIDERFace dataset using NVIDIA RTX 4090. YOLOv8 models were used as initial weights for training.

Name	Image Size (pixels)	mAPval 50-95	Params	GFLOPs
YOLOv8n-Face	640	37.5	3005843	8.1
YOLOv8s-Face	640	40.6	11125971	28.4
YOLOv8m-Face	640	41.7	25840339	78.7
YOLOv8l-Face	640	42.8	43607379	164.8
YOLOv8x-Face	640	43.3	68124531	257.4

• Download links:

Name	Model Size (MB)	Link	SHA-256
YOLOv8n-Face	6.0 11.7	PyTorch ONNX	b038ca653b503453a94f6e12d76feca6840b2a97d7a1322b4498c5e922f29832 8d0bfb0c3383c5bd7a78dd24ef79a21e2aa456619b6ab5e53867092d1c7dc414
YOLOv8s-Face	21.5 42.7	PyTorch ONNX	fa7e47fe9378255e4b52cb7abc4e387c0353dd26b0b8e6834045dc9dfbaaf69f 0a6d19f2f68d7f0cc8104ab5c9eaa54b63e298f91dcfefd4be897f94a1561d02
YOLOv8m-Face	49.6 98.8	PyTorch ONNX	303dcd997fb6ed446d1626b2bbd36f146894cdf600e33c4d563124f8c1b191c4 652f1ee6cd0291295de3d8fcaf9375ad62ef269055c0ada458bfdc4e7e6095da
YOLOv8l-Face	83.6 166.6	PyTorch ONNX	29cc43b27c8b865859c66489a4399a10a3efd80ce68ded9815364117641706d5 52dc39e46a7316398c95d30dd669a641382c9fdd8b675ad32aa65585bf820ea0
YOLOv8x-Face	130.4 260.1	PyTorch ONNX	117c587c79e75e68a83e70200549bf6c035fa45b30e02fb41699639aadcfa0e6 0ddad01728bc5f7d6c68c9b5567cfd1c8257f041af607c215ed865c5442f87fa

• Training results:

Name	Training Time	Epochs	Batch Size	Non-default parameters	Link
YOLOv8n-Face	2.75 hours	300	16	-	results.txt
YOLOv8s-Face	2.68 hours	200	16	-	results.txt
YOLOv8m-Face	3.01 hours	120	16	-	results.txt
YOLOv8l-Face	3.97 hours	110	16	-	results.txt
YOLOv8x-Face	13.65 hours	240	16	optimizer='SGD' lrf=1e-5 weight_decay=5e-3	results.txt

• Evaluation results on WIDERFace dataset:

Name	Easy	Medium	Hard
YOLOv8n-Face	93.79	91.82	79.38
YOLOv8s-Face	95.13	93.62	82.90
YOLOv8m-Face	95.73	94.47	84.55
YOLOv8l-Face	96.26	95.03	85.43
YOLOv8x-Face	96.33	95.16	85.80

Instructions

Installation

pip install -r requirements.txt

Prediction

python predict.py --weights weights/yolov8n-face-lindevs.pt --source data/images/bus.jpg

• OpenCV DNN

python examples/opencv-dnn-python/main.py --weights weights/yolov8n-face-lindevs.onnx --source data/images/bus.jpg

Export

• Install package:

pip install onnx

• Export to ONNX format:

python export.py --weights weights/yolov8n-face-lindevs.pt

• Or export to ONNX format using dynamic axis:

python export.py --weights weights/yolov8n-face-lindevs.pt --dynamic

Dataset Preparation

• Download WIDERFace dataset and annotations:

python download.py

• Convert annotations to YOLO format:

python annotations.py

• Copy widerface.yaml.example file to widerface.yaml:

python data_file.py

Training

• Prepare dataset.
• Start training:

python train.py --weights yolov8n.pt --epochs 300 2>&1 | tee -a results.txt
python train.py --weights yolov8s.pt --epochs 200 2>&1 | tee -a results.txt
python train.py --weights yolov8m.pt --epochs 120 2>&1 | tee -a results.txt
python train.py --weights yolov8l.pt --epochs 110 2>&1 | tee -a results.txt
python train.py --weights yolov8x.pt --epochs 240 --optimizer SGD --lrf 1e-5 --weight-decay 5e-3 2>&1 | tee -a results.txt

• Or resume training:

python train.py --weights runs/detect/train/weights/last.pt --resume 2>&1 | tee -a results.txt

Validation

• Prepare dataset.
• Start validation:

python validate.py --weights weights/yolov8n-face-lindevs.pt

WIDERFace Evaluation

• Prepare dataset.
• Start prediction on validation set:

python widerface/predict.py --weights weights/yolov8n-face-lindevs.pt

• Install package:

pip install Cython

• Build extension:

cd widerface && python setup.py build_ext --inplace && cd ..

• Start evaluation:

python widerface/evaluate.py