What is the significance of the Reg_Max parameter? How to adjust its value?

[xkangKK]

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Hello, I want to know the meaning of Reg_Max and how to adjust this parameter.
I looked at #2186, which describes "Reg_Max represents the maximum range of anchor parameters and can have an impact on small object detection in particular, but it can also help with larger objects that take up the entire image.” Unfortunately, I still don’t fully understand.
Could you please explain in detail the meaning of this parameter and its usage in different scenarios, such as 640640 image size, 12801280, or even larger or smaller resolutions, where there are large or small object?

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[glenn-jocher]

@xkangKK hello! The Reg_Max parameter is used to define the maximum range of anchor parameters. Anchor parameters are predefined bounding box sizes and aspect ratios that are used during training to detect objects of different sizes and shapes in your images.

The Reg_Max value is related to the scale of the anchors, meaning that a larger value of Reg_Max allows for larger anchor scales, which is useful for detecting larger objects. On the other hand, a smaller Reg_Max value allows for smaller anchor scales and can help with detecting smaller objects.

The optimal value for Reg_Max depends on the size and scale of the objects you want to detect in your images. Generally, larger image resolutions will require larger anchor scales, which may require increasing the Reg_Max value. You may need to experiment with different values of Reg_Max to determine the optimal value for your specific use case.

I hope this helps! Let me know if you have any further questions.

[xkangKK]

@xkangKK hello! The Reg_Max parameter is used to define the maximum range of anchor parameters. Anchor parameters are predefined bounding box sizes and aspect ratios that are used during training to detect objects of different sizes and shapes in your images.

The Reg_Max value is related to the scale of the anchors, meaning that a larger value of Reg_Max allows for larger anchor scales, which is useful for detecting larger objects. On the other hand, a smaller Reg_Max value allows for smaller anchor scales and can help with detecting smaller objects.

The optimal value for Reg_Max depends on the size and scale of the objects you want to detect in your images. Generally, larger image resolutions will require larger anchor scales, which may require increasing the Reg_Max value. You may need to experiment with different values of Reg_Max to determine the optimal value for your specific use case.

I hope this helps! Let me know if you have any further questions.

Thank you very much for your answer.

[glenn-jocher]

@xkangKK you're welcome! Don't hesitate to let me know if you have any further questions or concerns.

[tongchangD]

I try in different reg_amx in my data, at last,I find 32 is best ,my data is 1024x1280. but some mini smaller objects is not detect.

[glenn-jocher]

@tongchangD hello! It sounds like you have already experimented with different Reg_Max values and found that a value of 32 works best for your data, which has an image resolution of 1024x1280. However, you mentioned that some smaller objects are not being detected.

One possible reason for this could be that the anchor box sizes used during training are not well-suited for detecting small objects. Since objects of different sizes and aspect ratios require different anchor box sizes, it may be helpful to adjust the anchor box parameters to better match the size distribution of the objects in your dataset.

Another approach could be to try using a higher image resolution during training. This allows for the detection of smaller objects by providing more detailed input images, which can aid in the detection of fine details.

I hope this helps! If you have any further questions or concerns, please let me know.

[Rusab]

Hi @glenn-jocher, Isn't YOLOv8 anchor-box free? I am a bit confused why the anchor parameters that you are required in that case.

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