box,cls and dfl gain

[cnjhh]

How does box,cls,dfl set the weight, and why the default is 7.5, 0.5, 1.5? is there a basis for this? why the three weights don't add up to 1.

[glenn-jocher]

@cnjhh hello!

The box, cls, and dfl parameters in YOLOv8 are weights for different components of the loss function used during training. Here's a brief overview:

• box: This weight controls the importance of the bounding box regression loss.
• cls: This weight controls the importance of the classification loss.
• dfl: This weight controls the importance of the distribution focal loss.

The default values of 7.5, 0.5, and 1.5 are empirically chosen based on extensive experimentation to balance the contributions of each component effectively. These weights do not need to add up to 1 because they are not probabilities but rather scaling factors that adjust the relative importance of each loss component.

If you have further questions or need more details, feel free to ask! 😊

[cnjhh]

Thanks for your reply sir, I tweaked boxcls and dfl and trained on my own data, and something amazing happened, the modified model on the validation set was: 0.7710.6330.6970.423, the original model was: 0.750.6210.6990.439. Why is it that mAP50 -95 performance drops so much when both P and R values increase

[glenn-jocher]

Hello @cnjhh,

Thank you for sharing your results! It's great to hear that you've been experimenting with the box, cls, and dfl weights.

The mAP50-95 metric is a comprehensive measure that considers both precision and recall across different IoU thresholds. When you observe an increase in both precision (P) and recall (R) but a drop in mAP50-95, it could be due to several factors:

1. Balance Between Precision and Recall: While both P and R have increased, the balance between them might have shifted. mAP50-95 is sensitive to this balance, and even small changes can affect the overall score.

2. IoU Thresholds: mAP50-95 averages the precision and recall over multiple IoU thresholds (from 0.5 to 0.95). It's possible that while your model performs better at lower IoU thresholds, it might not perform as well at higher thresholds, leading to a lower average score.

3. Overfitting: Tweaking the weights might have caused the model to overfit to certain aspects of your validation set, improving P and R but not generalizing well across all IoU thresholds.

To further investigate, you might want to:

• Analyze the precision-recall curves at different IoU thresholds.
• Check if the model's performance is consistent across different classes and IoU thresholds.

If you have any more questions or need further assistance, feel free to ask! 😊