YOLOv9-Face

The YOLOv9-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] YOLOv9t-Face, YOLOv9s-Face, YOLOv9m-Face, YOLOv9c-Face and YOLOv9e-Face models has been added.

Pre-trained Models

The models have been trained on WIDERFace dataset using NVIDIA RTX 4090. YOLOv9 models were used as initial weights for training.

Name	Image Size (pixels)	mAPval 50-95	Params	GFLOPs
YOLOv9t-Face	640	37.0	1730019	6.4
YOLOv9s-Face	640	40.6	6194035	22.1
YOLOv9m-Face	640	42.5	16575715	60.0
YOLOv9c-Face	640	42.4	21146195	82.7
YOLOv9e-Face	640	43.3	53203347	169.5

• Download links:

Name	Model Size (MB)	Link	SHA-256
YOLOv9t-Face	4.0 7.0	PyTorch ONNX	3914713a5353d060eadc2cd8888676cc6ea9ac59921ed8bcff42755ee75a298c 7766f85cecd7045a1b64cf3a89d94819c62cc5ff24b782b86bb0dec4f9e31964
YOLOv9s-Face	12.7 24.0	PyTorch ONNX	f78b366a504b33f69e8b0ad0fc3c28e64153b167d71f4c1a29b903840fe67df4 9be9d734271868226274ea7e54f15e8c5bc2a4cf1b909a2eb6b6602987627e61
YOLOv9m-Face	32.4 63.5	PyTorch ONNX	bda08ea1388ae1d37747acb4dec4b08884b0078af2fc137fe4e93d498c474d3f a9a5775f869bc813402a37a690c50d9344520eda3177e4f66860370a68e5f23b
YOLOv9c-Face	41.3 81.0	PyTorch ONNX	b97c52c484ec873d0714615f566e31dc0bebc96eeafc43dca8204a9355802ba0 4e1cf66b2eade9240b5073d9563e6b737fe38123c4e53e342bec36274b530fae
YOLOv9e-Face	103.9 203.4	PyTorch ONNX	8f3410c7001dd73961a9c649d7dbd62162d0ac5851b54fd99deea4b9681abeed 4942ffc2f41358355913b85ac2f9aa033ec3d25a56546f6141500097d4a7b4f4

• Training results:

Name	Training Time	Epochs	Batch Size	Link
YOLOv9t-Face	4.40 hours	300	16	results.txt
YOLOv9s-Face	5.55 hours	300	16	results.txt
YOLOv9m-Face	5.61 hours	200	16	results.txt
YOLOv9c-Face	4.34 hours	130	16	results.txt
YOLOv9e-Face	5.04 hours	70	9	results.txt

• Evaluation results on WIDERFace dataset:

Name	Easy	Medium	Hard
YOLOv9t-Face	94.33	92.27	78.91
YOLOv9s-Face	95.54	94.08	83.06
YOLOv9m-Face	96.08	94.74	84.91
YOLOv9c-Face	96.28	95.09	85.47
YOLOv9e-Face	96.39	95.34	85.87

Instructions

Installation

pip install -r requirements.txt

Prediction

python predict.py --weights weights/yolov9t-face-lindevs.pt --source data/images/bus.jpg

• OpenCV DNN

python examples/opencv-dnn-python/main.py --weights weights/yolov9t-face-lindevs.onnx --source data/images/bus.jpg

Export

• Install package:

pip install onnx

• Export to ONNX format:

python export.py --weights weights/yolov9t-face-lindevs.pt

• Or export to ONNX format using dynamic axis:

python export.py --weights weights/yolov9t-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 yolov9t.pt --epochs 300 2>&1 | tee -a results.txt
python train.py --weights yolov9s.pt --epochs 300 2>&1 | tee -a results.txt
python train.py --weights yolov9m.pt --epochs 200 2>&1 | tee -a results.txt
python train.py --weights yolov9c.pt --epochs 130 2>&1 | tee -a results.txt
python train.py --weights yolov9e.pt --epochs 70 --batch 9 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/yolov9t-face-lindevs.pt

WIDERFace Evaluation

• Prepare dataset.
• Start prediction on validation set:

python widerface/predict.py --weights weights/yolov9t-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