search

jizhishutong / YOLOU

YOLOv3、YOLOv4、YOLOv5、YOLOv5-Lite、YOLOv6-v1、YOLOv6-v2、YOLOv7、YOLOX、YOLOX-Lite、PP-YOLOE、PP-PicoDet-Plus、YOLO-Fastest v2、FastestDet、YOLOv5-SPD、TensorRT、NCNN、Tengine、OpenVINO
GNU General Public License v3.0
743 stars 122 forks source link
ncnn openvino tengine tensorrt yolo yoloe yolov5 yolov6

readme

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.

YOLOU

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,......

Comparison of ablation experiment results | Model | size(pixels) | mAP@.5 | mAP@.5:95 | Parameters(M) | GFLOPs | TensorRT-FP32(b16)
ms/fps | TensorRT-FP16(b16)
ms/fps | |:------------------------------------------------------------------------------------------------|:------------:| :-------: | :-------: | :-----------: | :----: | :--------------------------: | :---------------------------: | | [YOLOv5n](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5n.pt) | 640 | 45.7 | 28.0 | 1.9 | 4.5 | 0.95/1054.64 | 0.61/1631.64 | | [YOLOv5s](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5s.pt) | 640 | 56.8 | 37.4 | 7.2 | 16.5 | 1.7/586.8 | 0.84/1186.42 | | [YOLOv5m](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5m.pt) | 640 | 64.1 | 45.4 | 21.2 | 49.0 | 4.03/248.12 | 1.42/704.20 | | [YOLOv5l](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5l.pt) | 640 | 67.3 | 49.0 | 46.5 | 109.1 | | | | [YOLOv5x](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5x.pt) | 640 | 68.9 | 50.7 | 86.7 | 205.7 | | | | YOLOv6-T | 640 | | | | | | | | YOLOv6-n | 640 | | | | | | | | YOLOv6 | 640 | 58.4 | 39.8 | 20.4 | 28.8 | 3.06/326.93 | 1.27/789.51 | | [YOLOv7](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7.pt) | 640 | 69.7 | 51.4 | 37.6 | 53.1 | 8.18/113.88 | 1.97/507.55 | | [YOLOv7-X](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7x.pt) | 640 | 71.2 | 53.7 | 71.3 | 95.1 | | | | [YOLOv7-W6](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-w6.pt) | 1280 | 72.6 | 54.9 | | | | | | [YOLOv7-E6](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-e6.pt) | 1280 | 73.5 | 56.0 | | | | | | [YOLOv7-D6](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-d6.pt) | 1280 | 74.0 | 56.6 | | | | | | [YOLOv7-E6E](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-e6e.pt) | 1280 | 74.4 | 56.8 | | | | | | YOLOX-s | 640 | 59.0 | 39.2 | 8.1 | 10.8 | 2.11/473.78 | 0.89/1127.67 | | YOLOX-m | 640 | 63.8 | 44.5 | 23.3 | 31.2 | 4.94/202.43 | 1.58/632.48 | | YOLOX-l | 640 | | | 54.1 | 77.7 | | | | YOLOX-x | 640 | | | 104.5 | 156.2 | | | | [v5-Lite-e](https://drive.google.com/file/d/1_DvT_qjznuE-ev_pDdGKwRV3MjZ3Zos8/view?usp=sharing) | 320 | 35.1 | | 0.78 | 0.73 | 0.55/1816.10 | 0.49/2048.47 | | [v5-Lite-s](https://drive.google.com/file/d/1ccLTmGB5AkKPjDOyxF3tW7JxGWemph9f/view?usp=sharing) | 416 | 42.0 | 25.2 | 1.64 | 1.66 | 0.72/1384.76 | 0.64/1567.36 | | [v5-Lite-c](https://drive.google.com/file/d/1lHYRQKjqKCRXghUjwWkUB0HQ8ccKH6qa/view?usp=sharing) | 512 | 50.9 | 32.5 | 4.57 | 5.92 | 1.18/850.03 | 0.80/1244.20 | | [v5-Lite-g](https://drive.google.com/file/d/1oftzqOREGqDCerf7DtD5BZp9YWELlkMe/view?usp=sharing) | 640 | 57.6 | 39.1 | 5.39 | 15.6 | 1.85/540.90 | 1.09/916.69 | | X-Lite-e | 320 | 36.4 | 21.2 | 2.53 | 1.58 | 0.65/1547.58 | 0.46/2156.38 | | X-Lite-s | 416 | Training… | Training… | 3.36 | 2.90 | | | | X-Lite-c | 512 | Training… | Training… | 6.25 | 5.92 | | | | X-Lite-g | 640 | 58.3 | 40.7 | 7.30 | 12.91 | 2.15/465.19 | 1.01/990.69 |

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

YOLOU

Segmentation Inference Result

YOLOU

KeyPoint Inference Result

YOLOU

Face-Detect Inference Result

YOLOU

DataSet ```bash train: ../coco/images/train2017/ val: ../coco/images/val2017/ ``` ```bash ├── 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

Expand ```bash python export.py --weights ./weights/yolov6/yolov6s.pt --include onnx ``` ​ In order to facilitate the deployment and implementation of friends here, all models included in YOLOU have been processed to a certain extent, and their pre- and post-processing codes can be used in one set, because the format and output results of the ONNX files they export are consistent. #### YOLOv5 ![YOLOU](./images/yolov5-onnx.png) #### YOLOv6 ![YOLOU](./images/yolov6-onnx.png) #### YOLOv7 ![YOLOU](./images/yolov7-onnx.png) #### YOLOX ![YOLOU](./images/yolox-onnx.png) #### YOLOv5-Lite ![YOLOU](./images/v5lite-onnx.png) #### YOLOX-Lite ![YOLOU](./images/yolox-lite-onnx.png) #### YOLO-Fastest v2 ![YOLOU](./images/yolo-fastest-v2.png)

Acknowledgements

Expand 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/shouxieai/tensorRT_Pro 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 https://github.com/meituan/YOLOv6/releases/tag/0.2.0 https://github.com/PaddlePaddle/PaddleDetection

Citing YOLOU

Expand If you use YOLOU 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} } ```

For more information, Please follow the QR code below：

YOLOU