YOLOv9代码解读[01] readme解读

文章目录

YOLOv9
- COCO数据集上指标：
- 环境安装
- 训练
- 验证
- [重参数化 Re-parameterization](#重参数化 Re-parameterization)
- 推断
- 相关链接

YOLOv9

paper: YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information
github: https://github.com/WongKinYiu/yolov9

COCO数据集上指标：

环境安装

docker环境

python 复制代码

# create the docker container, you can change the share memory size if you have more.
nvidia-docker run --name yolov9 -it -v your_coco_path/:/coco/ -v your_code_path/:/yolov9 --shm-size=64g nvcr.io/nvidia/pytorch:21.11-py3

# apt install required packages
apt update
apt install -y zip htop screen libgl1-mesa-glx

# pip install required packages
pip install seaborn thop

# go to code folder
cd /yolov9

训练

单GPU训练

python 复制代码

# train yolov9 models
python train_dual.py --workers 8 --device 0 --batch 16 --data data/coco.yaml --img 640 --cfg models/detect/yolov9-c.yaml --weights '' --name yolov9-c --hyp hyp.scratch-high.yaml --min-items 0 --epochs 500 --close-mosaic 15

# train gelan models
# python train.py --workers 8 --device 0 --batch 32 --data data/coco.yaml --img 640 --cfg models/detect/gelan-c.yaml --weights '' --name gelan-c --hyp hyp.scratch-high.yaml --min-items 0 --epochs 500 --close-mosaic 15

多GPU训练

python 复制代码

# train yolov9 models
python -m torch.distributed.launch --nproc_per_node 8 --master_port 9527 train_dual.py --workers 8 --device 0,1,2,3,4,5,6,7 --sync-bn --batch 128 --data data/coco.yaml --img 640 --cfg models/detect/yolov9-c.yaml --weights '' --name yolov9-c --hyp hyp.scratch-high.yaml --min-items 0 --epochs 500 --close-mosaic 15

# train gelan models
# python -m torch.distributed.launch --nproc_per_node 4 --master_port 9527 train.py --workers 8 --device 0,1,2,3 --sync-bn --batch 128 --data data/coco.yaml --img 640 --cfg models/detect/gelan-c.yaml --weights '' --name gelan-c --hyp hyp.scratch-high.yaml --min-items 0 --epochs 500 --close-mosaic 15

验证

python 复制代码

# evaluate converted yolov9 models
python val.py --data data/coco.yaml --img 640 --batch 32 --conf 0.001 --iou 0.7 --device 0 --weights './yolov9-c-converted.pt' --save-json --name yolov9_c_c_640_val

# evaluate yolov9 models
# python val_dual.py --data data/coco.yaml --img 640 --batch 32 --conf 0.001 --iou 0.7 --device 0 --weights './yolov9-c.pt' --save-json --name yolov9_c_640_val

# evaluate gelan models
# python val.py --data data/coco.yaml --img 640 --batch 32 --conf 0.001 --iou 0.7 --device 0 --weights './gelan-c.pt' --save-json --name gelan_c_640_val

重参数化 Re-parameterization

reparameterization.ipynb

推断

python 复制代码

# inference converted yolov9 models
python detect.py --source './data/images/horses.jpg' --img 640 --device 0 --weights './yolov9-c-converted.pt' --name yolov9_c_c_640_detect

# inference yolov9 models
# python detect_dual.py --source './data/images/horses.jpg' --img 640 --device 0 --weights './yolov9-c.pt' --name yolov9_c_640_detect

# inference gelan models
# python detect.py --source './data/images/horses.jpg' --img 640 --device 0 --weights './gelan-c.pt' --name gelan_c_c_640_detect

相关链接

code 复制代码

Custom training: https://github.com/WongKinYiu/yolov9/issues/30#issuecomment-1960955297
    
ONNX export: https://github.com/WongKinYiu/yolov9/issues/2#issuecomment-1960519506 https://github.com/WongKinYiu/yolov9/issues/40#issue-2150697688 https://github.com/WongKinYiu/yolov9/issues/130#issue-2162045461

TensorRT inference: https://github.com/WongKinYiu/yolov9/issues/143#issuecomment-1975049660 https://github.com/WongKinYiu/yolov9/issues/34#issue-2150393690 https://github.com/WongKinYiu/yolov9/issues/79#issue-2153547004 https://github.com/WongKinYiu/yolov9/issues/143#issue-2164002309

QAT TensirRT: https://github.com/WongKinYiu/yolov9/issues/253#issue-2189520073

OpenVINO: https://github.com/WongKinYiu/yolov9/issues/164#issue-2168540003

C# ONNX inference: https://github.com/WongKinYiu/yolov9/issues/95#issue-2155974619

C# OpenVINO inference: https://github.com/WongKinYiu/yolov9/issues/95#issuecomment-1968131244

OpenCV: https://github.com/WongKinYiu/yolov9/issues/113#issuecomment-1971327672

Hugging Face demo: https://github.com/WongKinYiu/yolov9/issues/45#issuecomment-1961496943

CoLab demo: https://github.com/WongKinYiu/yolov9/pull/18

ONNXSlim export: https://github.com/WongKinYiu/yolov9/pull/37

YOLOv9 ROS: https://github.com/WongKinYiu/yolov9/issues/144#issue-2164210644

YOLOv9 ROS TensorRT: https://github.com/WongKinYiu/yolov9/issues/145#issue-2164218595

YOLOv9 Julia: https://github.com/WongKinYiu/yolov9/issues/141#issuecomment-1973710107

YOLOv9 MLX: https://github.com/WongKinYiu/yolov9/issues/258#issue-2190586540

YOLOv9 ByteTrack: https://github.com/WongKinYiu/yolov9/issues/78#issue-2153512879

YOLOv9 DeepSORT: https://github.com/WongKinYiu/yolov9/issues/98#issue-2156172319

YOLOv9 counting: https://github.com/WongKinYiu/yolov9/issues/84#issue-2153904804

YOLOv9 face detection: https://github.com/WongKinYiu/yolov9/issues/121#issue-2160218766

YOLOv9 segmentation onnxruntime: https://github.com/WongKinYiu/yolov9/issues/151#issue-2165667350

Comet logging: https://github.com/WongKinYiu/yolov9/pull/110

MLflow logging: https://github.com/WongKinYiu/yolov9/pull/87

AnyLabeling tool: https://github.com/WongKinYiu/yolov9/issues/48#issue-2152139662

AX650N deploy: https://github.com/WongKinYiu/yolov9/issues/96#issue-2156115760

Conda environment: https://github.com/WongKinYiu/yolov9/pull/93

AutoDL docker environment: https://github.com/WongKinYiu/yolov9/issues/112#issue-2158203480