yolov8
开始安装
注意到安装比较简单，克隆项目，准备好 pytorch 环境，安装一个依赖即可。
尝试运行的命令？
yolo detect mode=predict model=yolov8l.pt source=ultralytics/assets/bus.jpg注意到该命令是相对于项目根目录下的绝对路径。执行完毕后，项目根目录下会出现一个名为 yolov8l.pt 的文件。
- try2
yolo detect mode=predict该命令不会去调用 cfg 配置声明的额外覆盖配置。没有找到合适的方式，实现复杂命令参数的统一配置。
根据教程开始搭建环境
笔记本电脑可用的命令行：
训练
yolo task=detect mode=train model=models/yolov8n.pt data=business/-temp-try/conf.yaml project=business/-temp-try/res batch=8 epochs=1 workers=8 imgsz=640验证
yolo task=detect mode=val model=business/-temp-try/res/train2/weights/best.pt data=business/-temp-try/conf.yaml project=business/-temp-try/res预测
yolo task=detect mode=predict model=business/-temp-try/res/train2/weights/best.pt source=business/-temp-try/images project=business/-temp-try/res目前的工作流
- 打标。不管用哪种打标工具，只要结果用 VOC，xml 格式导出即可。这远比单纯的 yolo，text 格式要好得多。
- 数据处理。拆分数据集和格式转换。事实上可以随便更改其次序。
- splitDataset.py 拆分数据
- xml-2-txt-chatgpt-v1.py 数据格式转换 同时录入打标的标签。
linux 训练
使用同一套运行命令。
linux 环境下常用的命令行
conda activate pytorch
cd /home/ai_rzn/code/ultralytics/
nohup 运行命令 &查看指定用户的进程
ps -u root在指定目录内，查看日志。这里默认特指 /home/ai_rzn/code/yolov5-master/ 目录
cat nohup.out常用的路径
数据集存储在 business 文件夹内
/home/ai_rzn/code/ultralytics/business/
/home/ai_rzn/code/ultralytics/
yolo task=detect mode=train model=models/yolov8n.pt data=business/person-drop-litter/conf.yaml project=business/person-drop-litter/res batch=8 epochs=1 workers=8 imgsz=640
训练：
nohup yolo task=detect mode=train model=models/yolov8n.pt data=business/person-drop-litter/conf.yaml project=business/person-drop-litter/res batch=8 epochs=350 workers=8 imgsz=640 &预测：
yolo task=detect mode=predict model=business/person-drop-litter/res/train2/weights/best.pt source=business/person-drop-litter/images project=business/person-drop-litter/resImportError: Cannot load backend 'TkAgg' which requires the 'tk' interactive framework, as 'headless' is currently running
在 linux 内出现此问题。在训练到最后时出现该错误：
Traceback (most recent call last):
File "/home/anaconda3/envs/pytorch/bin/yolo", line 33, in <module>
sys.exit(load_entry_point('ultralytics', 'console_scripts', 'yolo')())
File "/home/ai_rzn/code/ultralytics/ultralytics/yolo/cfg/__init__.py", line 398, in entrypoint
getattr(model, mode)(**overrides) # default args from model
File "/home/ai_rzn/code/ultralytics/ultralytics/yolo/engine/model.py", line 371, in train
self.trainer.train()
File "/home/ai_rzn/code/ultralytics/ultralytics/yolo/engine/trainer.py", line 192, in train
self._do_train(world_size)
File "/home/ai_rzn/code/ultralytics/ultralytics/yolo/engine/trainer.py", line 370, in _do_train
self.metrics, self.fitness = self.validate()
File "/home/ai_rzn/code/ultralytics/ultralytics/yolo/engine/trainer.py", line 476, in validate
metrics = self.validator(self)
File "/home/anaconda3/envs/pytorch/lib/python3.9/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context
return func(*args, **kwargs)
File "/home/ai_rzn/code/ultralytics/ultralytics/yolo/engine/validator.py", line 177, in __call__
stats = self.get_stats()
File "/home/ai_rzn/code/ultralytics/ultralytics/yolo/v8/detect/val.py", line 128, in get_stats
self.metrics.process(*stats)
File "/home/ai_rzn/code/ultralytics/ultralytics/yolo/utils/metrics.py", line 710, in process
results = ap_per_class(tp,
File "/home/ai_rzn/code/ultralytics/ultralytics/yolo/utils/metrics.py", line 523, in ap_per_class
plot_pr_curve(px, py, ap, save_dir / f'{prefix}PR_curve.png', names, on_plot=on_plot)
File "/home/ai_rzn/code/ultralytics/ultralytics/yolo/utils/__init__.py", line 197, in wrapper
plt.switch_backend(original_backend)
File "/home/anaconda3/envs/pytorch/lib/python3.9/site-packages/matplotlib/pyplot.py", line 279, in switch_backend
raise ImportError(
ImportError: Cannot load backend 'TkAgg' which requires the 'tk' interactive framework, as 'headless' is currently running- https://www.coder.work/article/6322766
- https://www.cnblogs.com/gy77/p/15740857.html
- https://github.com/open-mmlab/mmdetection/issues/5885
# 显示包版本
pip show matplotlib
# 移除
pip uninstall matplotlib
# 指定安装定版本
pip install matplotlib==3.2.1经过一系列的配置后，无论是 yolov5、slowfast、还是 yolov8。这些模型的多平台部署都让人非常恼火。现在打算学习 docker 来一次性完成环境部署了。累了。
其他同事在 matplotlib3.2.1 版本下，成功运行，其运行命令如下：
yolo task=detect mode=train model=yolov8n.pt data=business/person-drop-litter/conf.yaml batch=4 epochs=200 imgsz=640 workers=0YOLOv5 和 YOLOv8 使用体验
YOLOv8 用起来更加轻松，不用动脑子。
项目运行根目录 /home/ai_rzn/code/ultralytics/
图片文件路径 /home/ai_rzn/code/ultralytics/business/person-drop-litter/images/
训练配置文件路径 /home/ai_rzn/code/ultralytics/business/person-drop-litter/conf.yaml
实际执行的命令 nohup yolo task=detect mode=train model=models/yolov8n.pt data=business/person-drop-litter/conf.yaml project=business/person-drop-litter/res batch=8 epochs=350 workers=8 imgsz=640 &
贡献者
ruan-cat