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 target 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, YOLOv6, YOLOv7
Anchor-Free: YOLOX, YOLOX-Lite
Model | size(pixels) | [email protected] | [email protected]:95 | Parameters(M) | GFLOPs |
---|---|---|---|---|---|
YOLOv5n | 640 | 45.7 | 28.0 | 1.9 | 4.5 |
YOLOv5s | 640 | 56.8 | 37.4 | 7.2 | 16.5 |
YOLOv5m | 640 | 64.1 | 45.4 | 21.2 | 49.0 |
YOLOv5l | 640 | 67.3 | 49.0 | 46.5 | 109.1 |
YOLOv5x | 640 | 68.9 | 50.7 | 86.7 | 205.7 |
YOLOv6-T | 640 | ||||
YOLOv6-n | 640 | ||||
YOLOv6 | 640 | 57.3 | 38.7 | 20.4 | 28.8 |
YOLOv7 | 640 | 69.7 | 51.4 | 37.6 | 53.1 |
YOLOv7-X | 640 | 71.2 | 53.7 | 71.3 | 95.1 |
YOLOv7-W6 | 640 | 72.6 | 54.9 | ||
YOLOv7-E6 | 640 | 73.5 | 56.0 | ||
YOLOv7-D6 | 640 | 74.0 | 56.6 | ||
YOLOv7-E6E | 640 | 74.4 | 56.8 | ||
YOLOX-s | 640 | 59.0 | 39.2 | 8.1 | 10.8 |
YOLOX-m | 640 | Training… | Training… | 23.3 | 31.2 |
YOLOX-l | 640 | Training… | Training… | 54.1 | 77.7 |
YOLOX-x | 640 | Training… | Training… | 104.5 | 156.2 |
YOLOv5-Lite-e | 320 | 35.1 | 0.78 | 0.73 | |
YOLOv5-Lite-s | 416 | 42.0 | 25.2 | 1.64 | 1.66 |
YOLOv5-Lite-c | 512 | 50.9 | 32.5 | 4.57 | 5.92 |
YOLOv5-Lite-g | 640 | 57.6 | 39.1 | 5.39 | 15.6 |
YOLOX-Lite-e | 320 | 36.4 | 21.2 | 2.53 | 1.58 |
YOLOX-Lite-s | 416 | Training… | Training… | 3.36 | 2.90 |
YOLOX-Lite-c | 512 | Training… | Training… | 6.25 | 5.92 |
YOLOX-Lite-g | 640 | 58.3 | 40.7 | 7.30 | 12.91 |
git clone https://github.com/jizhishutong/YOLOU
cd YOLOU
pip install -r requirements.txt
python train.py --mode yolov6 --data coco.yaml --cfg yolov6.yaml --weights yolov6.pt --batch-size 32
python detect.py --source 0 # webcam
file.jpg # image
file.mp4 # video
path/ # directory
path/*.jpg # glob
'https://youtu.be/NUsoVlDFqZg' # YouTube
'rtsp:https://example.com/media.mp4' # RTSP, RTMP, HTTP stream
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
https://github.com/ultralytics/yolov5
https://github.com/WongKinYiu/yolor
https://github.com/ppogg/YOLOv5-Lite
https://github.com/WongKinYiu/yolov7
https://github.com/meituan/YOLOv6
https://github.com/ultralytics/yolov3
https://github.com/Megvii-BaseDetection/YOLOX
https://github.com/WongKinYiu/ScaledYOLOv4
https://github.com/WongKinYiu/PyTorch_YOLOv4
https://github.com/WongKinYiu/yolor
https://github.com/Tencent/ncnn
https://github.com/Gumpest/YOLOv5-Multibackbone-Compression
https://github.com/positive666/yolov5_research
https://github.com/cmdbug/YOLOv5_NCNN
https://github.com/OAID/Tengine
If you use YOLOv5-Lite in your research, please cite our work and give a star ⭐:
@misc{yolou2022,
title = { YOLOU:United, Study and easier to Deploy},
author = {ChaucerG},
year={2022}
}