Issue with relative paths in data.yaml file when trying to train yolo custom model

[bruhbot-dev]

I am trying to create a training pipeline to train a custom yolov9 model with user inputted labeled images.

I am having an issue where if I make my data.yaml file use relative paths, I get the error:

RuntimeError: Dataset 'OIT_model/customOIT/customdatasetyolo/data.yaml' error
  Dataset 'OIT_model/customOIT/customdatasetyolo/data.yaml' images not found , missing path 'C:\GitHub\Anomaly_detection_combine\OIT_model\Anomaly_detection_combine\OIT_model\customOIT\customdatasetyolo\Anomaly_detection_combine\OIT_model\customOIT\customdatasetyolo\val'

What is even more odd, is that the path the error mentions,

'C:\\GitHub\\Anomaly_detection_combine\\OIT_model\\Anomaly_detection_combine\\OIT_model\\customOIT\\customdatasetyolo\\Anomaly_detection_combine\\OIT_model\\customOIT\\customdatasetyolo\\val' is not a path that exists or is being requested anywhere. The actual path is

'C:\\GitHub\\Anomaly_detection_combine\\OIT_model\\customOIT\\customdatasetyolo\\val' for some reason it is repeating the first part of the path 3 times.

This is the data.yaml file:

    path: OIT_model/customOIT/customdatasetyolo
    train: OIT_model/customOIT/customdatasetyolo/train
    val: OIT_model/customOIT/customdatasetyolo/val
    nc: 1
    names: ['5']

and this is the code that is starting training:

    def train_custom_dataset_yolo(data_path, epochs=100, imgsz=64, verbose=True):
        model = YOLO("OIT_model/yolov9c.pt")
        # Specify the save directory for training runs
        save_dir = 'OIT_model/customOIT/yolocustomtrainoutput'
        if os.path.exists(save_dir):
            for file in os.listdir(save_dir):
                file_path = os.path.join(save_dir, file)
                if os.path.isfile(file_path) or os.path.islink(file_path):
                    os.unlink(file_path)
                elif os.path.isdir(file_path):
                    shutil.rmtree(file_path)
        os.makedirs(save_dir, exist_ok=True)
        model.train(data=data_path, epochs=epochs, imgsz=imgsz, verbose=verbose, save_dir=save_dir)
        return
    train_custom_dataset_yolo('OIT_model/customOIT/customdatasetyolo/data.yaml', epochs=1,imgsz=64, verbose=True)

Very strangely however, when I replace the relative paths with absolute paths, like so:

    path: C:/GitHub/fix/Anomaly_detection_combine/OIT_model/customOIT/customdatasetyolo
    train: C:/GitHub/fix/Anomaly_detection_combine/OIT_model/customOIT/customdatasetyolo/train
    val: C:/GitHub/fix/Anomaly_detection_combine/OIT_model/customOIT/customdatasetyolo/val
    nc: 1
    names: ['5']

training works without issue. Using absolute pathing is not an option for me, as this application needs to be reproductible on others machines.