yolov9训练自己的数据—vehicle 4类

1 conda环境

下载yolov9代码，并执行以下命令

$ git clone https://github.com/WongKinYiu/yolov9.git
$ cd yolov9
$ conda create --name yolov9 python=3.8
$ pip install -r requirement.txt
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安装指定版本torch

到pytorch官网下载安装。

pip install torch==2.0.0 torchvision==0.15.1 -index-url https://download.pytorch.org/whl/cu118
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torch安装失败，到上面地址手动下载v2.0.0，再安装。

 $ pip install torch-2.0.0+cu118-cp38-cp38-linux_x86_64.whl
 $ pip install torchvision-0.15.1+cu118-cp38-cp38-linux_x86_64.whl
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2 预训练模型测试

# inference converted yolov9 models
$ python detect.py --source ./data/images/horses.jpg --img 640 --device 0 --weights ./weights/yolov9-c-converted.pt --conf 0.1

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

# inference gelan models
# python detect.py --source ./data/images/horses.jpg --img 640 --device 0 --weights ./weights/gelan-c.pt
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detect: weights=['./weights/yolov9-c-converted.pt'], source=./data/images/horses.jpg, data=data/coco128.yaml, imgsz=[640, 640], conf_thres=0.25, iou_thres=0.4e, save_txt=False, save_conf=False, save_crop=False, nosave=False, classes=None, agnostic_nms=False, augment=False, visualize=False, update=False, project=run_thickness=3, hide_labels=False, hide_conf=False, half=False, dnn=False, vid_stride=1
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