Problems in yolov8 training about ultralytics HOT 1 OPEN

It looks like you're encountering a type mismatch during the validation phase where the input tensor and weights are of different data types. This is common when you use mixed precision training, as certain tensors might be converted to half-precision (torch.cuda.HalfTensor) while others remain in full precision (torch.cuda.FloatTensor).

You can try ensuring that both your model and inputs are consistent in their data types. You might consider explicitly setting your model to use .float() or .half() before training to handle precision uniformly. Here’s a specific modification you can make at the beginning of your training loop:

from ultralytics import YOLO

if __name__ == "__main__":
    model = YOLO('path/to/your/yolov8.yaml').float()  # Ensure model uses float32
    results = model.train(data='path/to/your/dataset.yaml', epochs=100, imgsz=640, batch=2)

This modification ensures that your model uses 32-bit floating point precision consistently. If you intend to use mixed precision to leverage speedups from .half(), make sure that your input data tensors and your model agree on the data type throughout the training process. If you're still facing issues or need further assistance, please let us know! 😊

from ultralytics.