what's difference between channel attention built in yolov8 with the one designed in CBAM?

[zeobec]

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Question

from CBAM paper, channel attention built by using both max and average pooling:

however, I found in yolov8, channel attention built is different from the one described in CBAM paper where only average pooling is used.

does it mean CBAM that built in yolov8 is different with the original CBAM? can anyone explain to me?

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[pderrenger]

@zeobec the channel attention mechanism in YOLOv8 indeed differs from the one described in the CBAM paper. While CBAM uses both max and average pooling to generate the attention maps, YOLOv8 employs only average pooling. This design choice simplifies the attention mechanism, potentially reducing computational overhead while still capturing essential channel-wise information. Therefore, the implementation in YOLOv8 is a variation of the original CBAM, tailored to balance performance and efficiency for the specific needs of the model. If you have further questions or need more details, feel free to ask!

[zeobec]

great! thankyou for the explanation @pderrenger I tried to train by implementing CBAM from the paper, i changed with the one that built in YOLOv8. however, I got this but the training still proceeds like usual. Is that okay if I just ignore the warning? what's the meaning of the warning actually? and I do realize that maybe because of this, new variation of CBAM is implemented in YOLOv8

[pderrenger]

@zeobec you're welcome! The warning you're seeing typically indicates a potential issue with the model architecture or a mismatch in expected input dimensions. While the training may proceed, it's essential to understand the root cause to ensure the integrity of your results. The new variation of CBAM in YOLOv8 is designed to be more efficient and compatible with the model's architecture. Ignoring the warning might lead to suboptimal performance or unexpected behavior. If the issue persists, I recommend checking the latest package versions to see if the problem is resolved. If you need further assistance, feel free to ask!

[zeobec]

I have downloaded the latest version of ultralytics so probably that's not the issue. so the solution is it I need to find a way to make sure the adaptive max pool2d backward cuda have a deterministic implementation?

[pderrenger]

@zeobec thank you for the update. The warning you're encountering suggests a non-deterministic behavior in the CUDA implementation of adaptive_max_pool2d. This can sometimes lead to slight variations in results across different runs. While this might not significantly impact your training, ensuring deterministic behavior can be crucial for reproducibility. You might want to explore setting deterministic flags in PyTorch or using alternative pooling methods that guarantee deterministic behavior. If the issue persists, consider checking the PyTorch documentation or forums for more detailed guidance on handling non-deterministic operations.