How can I handle images with a resolution of 3840*2160 directly?

[shengyuqing]

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Question

The primary application is detecting small defects in products, where reducing the size, for example to 640, would compromise the detection capability."

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[glenn-jocher]

You can handle high-resolution images directly by setting the imgsz parameter to your desired resolution (3840x2160) during inference or training. However, processing such high-resolution images can be computationally expensive, so ensure your hardware setup can handle it. Here’s an example of how you can set this up:

yolo detect predict model=yolov8n.pt source='path/to/high-res.jpg' imgsz=3840,2160

Adjust your model’s architecture and training parameters for best results with high-resolution images, especially for small object detection. If performance is an issue, consider using techniques like a sliding window approach or image tiling.

[shengyuqing]

You can handle high-resolution images directly by setting the imgsz parameter to your desired resolution (3840x2160) during inference or training. However, processing such high-resolution images can be computationally expensive, so ensure your hardware setup can handle it. Here’s an example of how you can set this up:

yolo detect predict model=yolov8n.pt source='path/to/high-res.jpg' imgsz=3840,2160

Adjust your model’s architecture and training parameters for best results with high-resolution images, especially for small object detection. If performance is an issue, consider using techniques like a sliding window approach or image tiling.

Thank you very much for your answer to my question. Okay, I'll try it with the original image first. Another point is that the current defect detection is rectangular. Do I need to adjust it to a square image during training and inference?

[glenn-jocher]

@shengyuqing no need to adjust your rectangular images to square. YOLOv8 can handle different aspect ratios effectively. Just set the imgsz parameter to your image’s resolution. Here's an example command for running inference with original rectangular dimensions:

yolo detect predict model=yolov8n.pt source='path/to/rectangular-image.jpg' imgsz=3840,2160

This approach should work well without modifying the shape of your images. Give it a try and see how it performs! 😊

[shengyuqing]

@glenn-jocher Thank you very much for your reply. What I used for training before was model.train(data='data/train_Normal.yaml', epochs=epochs, workers=0,save=True,device=gpus,imgsz=500,batch=batch,close_mosaic=1) This line of code, how to change“ imgsz=500 ”to “imgsz=3840,2160”？