YOLOU：United, Study and easier to Deploy

​ The purpose of our creation of YOLOU is to better learn the algorithms of the YOLO series and pay tribute to our predecessors.

​ Here "U" means United, mainly to gather more algorithms about the YOLO series through this project, so that friends can better learn the knowledge of object detection. At the same time, in order to better apply AI technology, YOLOU will also join The corresponding Deploy technology will accelerate the implementation of the algorithms we have learned and realize the value.

At present, the YOLO series algorithms mainly included in YOLOU are:

Anchor-base: YOLOv3, YOLOv4, YOLOv5, YOLOv5-Lite, YOLOv7, YOLOv5-TPH, YOLO-Fastest v2, YOLO-LF, YOLO-SA, YOLOR, YOLOv5-SPD

Anchor-Free: YOLOv6-v1, YOLOv6-v2, YOLOX, YOLOE, YOLOX-Lite, FastestDet

Face-Detection: YOLOv5-Face, YOLOFace-v2

Segmentation: YOLOv5-Segment

KeyPoint: YOLOv7-Keypoint

Classfication: ResNet, DarkNet,......

How to use

Install

git clone https://github.com/jizhishutong/YOLOU
cd YOLOU
pip install -r requirements.txt

Training

python train_det.py --mode yolov6 --data coco.yaml --cfg yolov6.yaml --weights yolov6.pt --batch-size 32

Detect

python detect_det.py --source 0  # webcam
                            file.jpg  # image 
                            file.mp4  # video
                            path/  # directory
                            path/*.jpg  # glob
                            'https://youtu.be/NUsoVlDFqZg'  # YouTube
                            'rtsp://example.com/media.mp4'  # RTSP, RTMP, HTTP stream

Re-parameterization

See reparameterization.ipynb

Pose estimation

yolov7-w6-pose.pt

See keypoint.ipynb.

Detect Inference Result

Segmentation Inference Result

KeyPoint Inference Result

Face-Detect Inference Result

train: ../coco/images/train2017/
val: ../coco/images/val2017/

├── images            # xx.jpg example
│   ├── train2017        
│   │   ├── 000001.jpg
│   │   ├── 000002.jpg
│   │   └── 000003.jpg
│   └── val2017         
│       ├── 100001.jpg
│       ├── 100002.jpg
│       └── 100003.jpg
└── labels             # xx.txt example      
    ├── train2017       
    │   ├── 000001.txt
    │   ├── 000002.txt
    │   └── 000003.txt
    └── val2017         
        ├── 100001.txt
        ├── 100002.txt
        └── 100003.txt

Export ONNX

python export.py --weights ./weights/yolov6/yolov6s.pt --include onnx

Acknowledgements

Citing YOLOU

 @misc{yolou2022,
  title = { YOLOU：United, Study and easier to Deploy},
  author = {ChaucerG},
  year={2022}
}

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