Confusion Matrix Issue After Training

[smtalds-knt]

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Question

I conducted a 300-epoch training using the BDD100K dataset. My test results are shown below. However, there seems to be a problem with the confusion matrix — the predictions appear incorrect or inconsistent.

Note: Before the training phase, I followed the instructions in this GitHub issue comment: https://github.com/Xilinx/Vitis-AI/issues/1252#issuecomment-1608431434 Specifically, I replaced the SiLU activation layer as recommended for compatibility with Vitis AI.

Could this modification be related to the issue with the confusion matrix?

Additional

No response

[UltralyticsAssistant]

👋 Hello @smtalds-knt, thank you for your interest in YOLOv5 🚀! Please visit our ⭐️ Tutorials to get started, where you can find quickstart guides for simple tasks like Custom Data Training all the way to advanced concepts like Hyperparameter Evolution.

If this is a 🐛 Bug Report, please provide a minimum reproducible example (MRE) that clearly demonstrates the confusion matrix issue, including:

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[pderrenger]

Yes, replacing the SiLU activation function can definitely impact your model's performance and confusion matrix results. SiLU is the default activation in YOLOv5 for good reason - it provides smooth gradients and typically better convergence compared to alternatives like ReLU.

To verify if the activation change is causing the confusion matrix inconsistencies, I'd recommend training a baseline model with the original SiLU activations on the same BDD100K dataset subset and comparing the confusion matrix results. Also ensure you're using the latest YOLOv5 version as there have been improvements to validation metrics calculation.

The Vitis AI compatibility modification may be necessary for your deployment target, but it's likely contributing to the performance differences you're observing.

[smtalds-knt]

Hi @pderrenger ,

Thank you for your response.
I am now able to get correct detections with high confidence scores from the FPGA devices — it's working well for me.

I had to avoid using SiLU, as it is not supported by Vitis AI.

Best regards,
Samet

[pderrenger]

Great to hear that your FPGA deployment is working well with high confidence detections! You're absolutely right that avoiding SiLU was necessary for Vitis AI compatibility - this is a known trade-off when deploying to FPGA targets where certain activation functions aren't supported.

It sounds like you've successfully navigated the compatibility requirements while maintaining good model performance for your specific deployment scenario.