Docker中新建Python容器运行yolov9

1. 下载python docker镜像

docker pull python

1. 运行python docker容器

docker run -it name python3 python:latest

1. 进入python docker命令行

docker exec -it python3 /bin/bash

1. 从Github克隆yolov9源

git clone …

1. 进入yolov9源内部

cd …

1. 安装yolov9所需的依赖项

pip install -r requirements.txt -i Simple Index

1. 安装ultralytics

pip install ultralytics -i Simple Index

1. 运行python detect.py

python detect.py

此时会遇到如下问题：

ImportError: libGL.so.1: cannot open shared object file: No such file or directory

按照网上的[【解决方法】libGL.so.1: cannot open shared object file: No such file or directory-CSDN博客]解决该问题。

再次执行步骤8，得到如下结果：

root@fbf0bab596fb:/yolov9# python detect.py
detect: weights=yolo.pt, source=data/images, data=data/coco128.yaml, imgsz=[640, 640], conf_thres=0.25, iou_thres=0.45, max_det=1000, device=, view_img=False, save_txt=False, save_conf=False, save_crop=False, nosave=False, classes=None, agnostic_nms=False, augment=False, visualize=False, update=False, project=runs/detect, name=exp, exist_ok=False, line_thickness=3, hide_labels=False, hide_conf=False, half=False, dnn=False, vid_stride=1
YOLO 🚀 ac63ee5 Python-3.12.2 torch-2.2.2+cu121 CPU

YOLOv8n summary (fused): 168 layers, 3151904 parameters, 0 gradients, 8.7 GFLOPs
image 1/1 /yolov9/data/images/horses.jpg: 448x640 5 horses, 155.9ms
Speed: 0.9ms pre-process, 155.9ms inference, 3.2ms NMS per image at shape (1, 3, 640, 640)
Results saved to runs/detect/exp2

到此结束！