Input questions about yolov8

[DJohn1211]

Thank you for your contributions to the field of computer vision. I am a student in the field of fluid. Last year, I realized very good image segmentation with the help of yolov8, which is of great help to my research. Recently, I have a new idea that I would like to ask you about. In the input of yolov8, both training and prediction are to input an image into the model, that is, the input is a 6406403 matrix. When I process some experimental images in the field of fluid, I can get the distribution of the velocity field of the whole image (horizontal velocity and vertical velocity). So I have the following considerations, I also adjust the format of the velocity field to 6406402, and I combine them together to get a 6406405 matrix. I want to use yolov8 to do an image segmentation on fluid boundary. My idea to do this is that at the segmentation boundary, not only the pixel changes, but also the velocity field will have obvious changes. Can my processing be helpful to model training, and what should I pay attention to if I do this? Looking forward to your reply

[UltralyticsAssistant]

👋 Hello @DJohn1211, thank you for sharing your fascinating research application involving YOLOv8 and fluid dynamics 🚀! Your idea of combining image data with velocity fields to enhance image segmentation is intriguing and could potentially offer new insights.

For new endeavors like yours, I suggest exploring the Docs to gather detailed information on using YOLOv8 for segmentation tasks. It might help to look into creative data input methods and how such inputs might impact model performance.

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[DJohn1211]

The image resembles this situation, with the bright line in the middle being the obvious boundary

[glenn-jocher]

@DJohn1211 thank you for sharing your interesting approach to fluid boundary segmentation. Your idea of combining image data (3 channels) with velocity field data (2 channels) into a 640x640x5 input is valid and could potentially improve model performance. YOLOv8's architecture can be modified to accept different input channel counts by adjusting the first convolution layer. The velocity field information could indeed provide valuable features for detecting boundaries since fluid dynamics often show distinct velocity gradients at interfaces.

To implement this:

1. You'll need to modify the model configuration to accept 5 input channels instead of 3
2. Ensure your velocity field data is properly normalized to a similar scale as the image data
3. Validate that the velocity field registration aligns perfectly with the image data

For a detailed guide on modifying YOLOv8's input channels, please see the documentation at https://docs.ultralytics.com/tasks/segment/ under the "Custom Data" section.

This is an innovative application of YOLOv8 for fluid dynamics research. I'd be interested to hear about your results if you pursue this approach.