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English | 简体中文

YOLOv6-Face

Implementation based on YOLOv6 v3.0 code.

New Feature

  • Face-landmarks localization
  • Repulsion loss
  • Same-channel Dehead

Performance on WIDERFACE

Model Size Easy Medium Hard SpeedT4
trt fp16 b1
(fps)		 SpeedT4
trt fp16 b32
(fps)		 Params
(M)		 FLOPs
(G)
YOLOv6-N 640 95.0 92.4 80.4 797 1313 4.63 11.35
YOLOv6-S 640 96.2 94.7 85.1 339 484 12.41 32.45
YOLOv6-M 640 97.0 95.3 86.3 188 240 24.85 70.59
YOLOv6-L 640 97.2 95.9 87.5 102 121 56.77 159.24
YOLOv6Lite-S 416 89.6 84.6 58.8 / / 0.53 0.90
YOLOv6Lite-M 416 90.6 86.1 60.6 / / 0.76 1.07
YOLOv6Lite-L 416 91.8 87.6 64.2 / / 1.06 1.40

Table Notes

  • All checkpoints are fine-tuned from COCO pretrained model for 300 epochs without distillation.
  • Results of the mAP and speed are evaluated on WIDER FACE dataset with the input resolution of 640×640.
  • Speed is tested with TensorRT 8.2 on T4.
  • Refer to Test speed tutorial to reproduce the speed results of YOLOv6.
  • Params and FLOPs of YOLOv6 are estimated on deployed models.

Quick Start

Install 
git clone https://github.com/meituan/YOLOv6
cd YOLOv6
git checkout yolov6-face
pip install -r requirements.txt
Training

Single GPU

python tools/train.py --batch 8 --conf configs/yolov6s_finetune.py --data data/WIDER_FACE.yaml --fuse_ab --device 0

Multi GPUs (DDP mode recommended)

python -m torch.distributed.launch --nproc_per_node 8 tools/train.py --batch 64 --conf configs/yolov6s_finetune.py --data data/WIDER_FACE.yaml --fuse_ab --device 0,1,2,3,4,5,6,7
  • fuse_ab: Anchor Aided Training Mode
  • conf: select config file to specify network/optimizer/hyperparameters. We recommend to apply yolov6n/s/m/l_finetune.py when training on WIDER FACE or your custom dataset.
  • data: prepare dataset and specify dataset paths in data.yaml ( WIDERFACE, YOLO format widerface labels )
  • make sure your dataset structure as follows:
├── widerface
│   ├── images
│   │   ├── train
│   │   └── val
│   ├── labels
│   │   ├── train
│   │   ├── val
Inference

First, download a pretrained model from the YOLOv6 release or use your trained model to do inference.

Second, run inference with tools/infer.py

python tools/infer.py --weights yolov6s_face.pt --source ../widerface/images/val/ --yaml data/WIDER_FACE.yaml --conf 0.02 --not-save-img --save-txt-widerface --name widerface_yolov6s
Evaluation 
cd widerface_evaluate
python evaluation.py --pred ../runs/inference/widerface_yolov6s/labels/
Deployment
Tutorials
Third-party resources

FAQ（Continuously updated）

If you have any questions, welcome to join our WeChat group to discuss and exchange.