yolov8模型训练好以后直接调用Ultralytics中的YOLO进行推理是怎么把图像缩放到训练时的图像大小的，这个参数被保存在哪里？

[yangyang-03120100]

After the yolov8 model is trained, it directly calls YOLO in Ultralytics for inference. How is the image scaled to the trained image size? Where is this parameter saved?

[glenn-jocher]

@yangyang-03120100 hello!

Great question! When you train a YOLOv8 model, the image size used during training is indeed an important parameter. This parameter is stored within the model's configuration and is automatically utilized during inference to ensure consistency.

Here's how it works:

1. Training Configuration: During training, you specify the image size using the imgsz parameter. This parameter is saved in the model's configuration file.

2. Inference: When you load the trained model for inference using YOLO("path/to/your_model.pt"), the model retains the image size used during training. This ensures that the input images are resized to the same dimensions as during training.

For example, if you trained your model with an image size of 640, it will automatically resize input images to 640x640 during inference.

Here's a quick code snippet to illustrate this:

from ultralytics import YOLO

# Load your trained model
model = YOLO("path/to/your_model.pt")

# Run inference on an image
results = model("path/to/your_image.jpg")

The model will handle the resizing internally based on the configuration saved during training.

If you want to explicitly check or modify the image size during inference, you can do so by specifying the imgsz parameter:

results = model("path/to/your_image.jpg", imgsz=640)

This flexibility allows you to adapt the inference process as needed while maintaining the integrity of the trained model's configuration.

For more detailed information, you can refer to the YOLOv8 documentation.

If you encounter any issues or need further assistance, feel free to provide a reproducible example, and we'll be happy to help! 😊