YOLOv8：RuntimeError：在当前进程完成其引导阶段之前已尝试启动新进程

Question

已尝试在 当前进程已完成引导阶段。

    This probably means that you are not using fork to start your
    child processes and you have forgotten to use the proper idiom
    in the main module:

        if __name__ == '__main__':
            freeze_support()
            ...

    The "freeze_support()" line can be omitted if the program
    is not going to be frozen to produce an executable.

** 尝试在 python 环境中训练 YOLOv8 模型时出现此错误** 从 ultralytics 导入 YOLO

# Load a model
model = YOLO("yolov8n.yaml")  # build a new model from scratch
model = YOLO("yolov8n.pt")  # load a pretrained model (recommended for training)

# Use the model
results = model.train(data="coco128.yaml", epochs=3)  # train the model
results = model.val()  # evaluate model performance on the validation set
results = model("https://ultralytics.com/images/bus.jpg")  # predict on an image
success = YOLO("yolov8n.pt").export(format="onnx")  # export a model to ONNX format

Answer 1

我设法通过将模型过程代码保留在顶级环境的名称下（如果 name == 'main':) 来克服这个问题，如下所示：

from ultralytics import YOLO

# Load a model
model = YOLO("yolov8n.yaml")  # build a new model from scratch
model = YOLO("yolov8n.pt")  # load a pretrained model (recommended for training)

if __name__ == '__main__':
    # Use the model
    results = model.train(data="coco128.yaml", epochs=3)  # train the model
    results = model.val()  # evaluate model performance on the validation set
    results = model("https://ultralytics.com/images/bus.jpg")  # predict on an image
    success = YOLO("yolov8n.pt").export(format="onnx")  # export a model to ONNX format

Answer 2

我有同样的错误，我没有使用 python 脚本，而是使用 CLI 进行培训。

从 ultralytics 目录转到终端： yolo task=detect mode=train epochs=100 data=yolov8n.yaml model=yolov8n.pt imgsz=640