Whether to support anchor-base about ultralytics

[glenn-jocher]

@ChenQD-0627 hi there!

YOLOv8 primarily uses anchor-free mechanisms for object detection, which simplifies the model and often improves performance. However, if you're looking for anchor-based support, you might want to explore earlier versions like YOLOv5, which do support anchor-based detection.

For YOLOv5, you can set custom anchors in the model configuration file (.yaml). Here's a quick example of how you can define custom anchors:

anchors:
  - [10,13, 16,30, 33,23]  # P3/8
  - [30,61, 62,45, 59,119]  # P4/16
  - [116,90, 156,198, 373,326]  # P5/32

If you have any further questions or need assistance with YOLOv8 or other versions, feel free to ask! 😊

[ChenQD-0627]

I want to integrate anchor-base into yolov8, but I can't find the key code to replace, can you give me some guidance?

[glenn-jocher]

Hi @ChenQD-0627,

Thank you for your question! YOLOv8 is designed to be anchor-free, which simplifies the model and often enhances performance. However, if you are keen on integrating anchor-based mechanisms, you might need to delve into the model's architecture and modify it accordingly.

Here's a general approach to guide you:

1. Understand the YOLOv8 Architecture: Familiarize yourself with the YOLOv8 model structure. You can start by exploring the model definition files in the Ultralytics repository.

2. Identify Key Components: Locate the parts of the code where the model defines its detection head. This is where you would typically integrate anchor-based logic.

3. Modify the Detection Head: Replace the anchor-free head with an anchor-based one. You can refer to the YOLOv5 implementation for inspiration, as it supports anchor-based detection. Specifically, look into how YOLOv5 defines and uses anchors in its head.

4. Adjust Training and Inference Logic: Ensure that the training and inference processes are compatible with the anchor-based approach. This might involve changes in how bounding boxes are predicted and processed.

Unfortunately, providing a detailed step-by-step code modification guide is beyond the scope of this platform. However, I encourage you to explore the YOLOv5 codebase for insights and adapt similar strategies in YOLOv8.

If you encounter specific issues or have further questions during your implementation, feel free to ask!

Best of luck with your integration! 😊

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