Parameters in Yolov8

[VuongMinhTuan]

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Question

I'm studying the architecture of yolov8 now and I'm trying to figure out why you decided to choose those parameters in each layers/blocks. Until now, I still don't understand.

For example: In SPPF block, there are Max Pooling layers. Why did you set kernel_size=k, stride=1 and padding=k with k=5?

OR

In SPPF block, why did you set c_ = c2 // 2, not another number? (c_ is hidden channel and c2 is out_channel)

Are those parameters chosen based on something or you just tested many times for figuring out

Thanks for reading (I'm sorry if my questions are not clear because I'm not good at English)

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[github-actions]

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[glenn-jocher]

Hello! Thanks for your interest in the YOLOv8 architecture and for your questions on the parameters used in its layers.

The parameters in each layer, such as the SPPF block, are selected primarily based on empirical evidence gathered through extensive testing, which includes experiments for optimizing both speed and accuracy. Setting kernel_size=k, stride=1, and padding=k in the Max Pooling layers of the SPPF block helps in maintaining the spatial size of the features, allowing us to extract granular features at different scales effectively.

Regarding the choice of c_ = c2 // 2, this is a common strategy to reduce the number of channels by half, thereby reducing the computational complexity while still retaining sufficient information for the task at hand. This balance helps to maintain performance while improving efficiency.

Every design choice typically aims to balance between computational efficiency and performance efficacy, often refined through various stages of prototype testing and validation against benchmark datasets.

Feel free to examine the further discussions on such architectural decisions in our GitHub discussions or the relevant technical papers linked in the repository for deeper insights! 😊