How to set different loss weights

[Vayne0227]

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Question

Hello author, I would like to know how to set the weight coefficients for L1 loss, giou loss, and class loss

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[glenn-jocher]

@Vayne0227 hi there!

To set different weight coefficients for L1 loss, GIoU loss, and class loss in YOLOv8, you can modify the hyperparameters in your training configuration file. Here’s a quick guide on how to do it:

1. Locate or Create a Custom Configuration File: If you haven't already, you can create a custom configuration file by copying the default default.yaml using the command:

   yolo copy-cfg

2. Modify the Hyperparameters: Open your custom configuration file (e.g., default_copy.yaml) and look for the hyperparameters related to the losses. You can set the weights for the losses as follows:

   # Example custom configuration
   box: 7.5  # Weight for the box loss component
   cls: 0.5  # Weight for the classification loss
   dfl: 1.5  # Weight for the distribution focal loss

3. Use the Custom Configuration File: When you start training, specify your custom configuration file:

   yolo train cfg=default_copy.yaml

This will allow you to adjust the loss weights according to your needs. If you encounter any issues or need further assistance, feel free to provide a reproducible example so we can help you more effectively. You can find more details on creating a minimum reproducible example here.

Happy training! 🚀

[Vayne0227]

@Vayne0227 hi there!

To set different weight coefficients for L1 loss, GIoU loss, and class loss in YOLOv8, you can modify the hyperparameters in your training configuration file. Here’s a quick guide on how to do it:

1. Locate or Create a Custom Configuration File: If you haven't already, you can create a custom configuration file by copying the default default.yaml using the command:

   yolo copy-cfg

2. Modify the Hyperparameters: Open your custom configuration file (e.g., default_copy.yaml) and look for the hyperparameters related to the losses. You can set the weights for the losses as follows:

   # Example custom configuration
   box: 7.5  # Weight for the box loss component
   cls: 0.5  # Weight for the classification loss
   dfl: 1.5  # Weight for the distribution focal loss

3. Use the Custom Configuration File: When you start training, specify your custom configuration file:

   yolo train cfg=default_copy.yaml

This will allow you to adjust the loss weights according to your needs. If you encounter any issues or need further assistance, feel free to provide a reproducible example so we can help you more effectively. You can find more details on creating a minimum reproducible example here.

Happy training! 🚀

Thank you for your reply, but I am not sure which of these three weight coefficients corresponds to L1 loss, GIoU loss, and category loss. Can you help me explain this?

[glenn-jocher]

Hi @Vayne0227,

Absolutely, I'd be happy to clarify that for you! In the context of YOLOv8, the weight coefficients for the different loss components are as follows:

• Box Loss (GIoU Loss): This is controlled by the box hyperparameter. It determines the weight of the bounding box regression loss, which typically uses GIoU (Generalized Intersection over Union).
• Classification Loss: This is controlled by the cls hyperparameter. It sets the weight for the classification loss, which is responsible for correctly classifying the detected objects.
• L1 Loss: This is not directly specified as a separate hyperparameter in YOLOv8. Instead, the dfl (Distribution Focal Loss) hyperparameter is used, which is related to the fine-grained localization of bounding boxes.

Here’s how you can set these in your custom configuration file:

# Example custom configuration
box: 7.5  # Weight for the GIoU loss component
cls: 0.5  # Weight for the classification loss
dfl: 1.5  # Weight for the distribution focal loss

To summarize:

• box corresponds to the GIoU loss.
• cls corresponds to the classification loss.
• dfl is related to the localization aspect, which can be considered as part of the bounding box refinement.

If you have any further questions or need additional assistance, feel free to ask. Happy training! 🚀

[WZJAI2018]

Hi @Vayne0227,

Absolutely, I'd be happy to clarify that for you! In the context of YOLOv8, the weight coefficients for the different loss components are as follows:

* **Box Loss (GIoU Loss)**: This is controlled by the `box` hyperparameter. It determines the weight of the bounding box regression loss, which typically uses GIoU (Generalized Intersection over Union).

* **Classification Loss**: This is controlled by the `cls` hyperparameter. It sets the weight for the classification loss, which is responsible for correctly classifying the detected objects.

* **L1 Loss**: This is not directly specified as a separate hyperparameter in YOLOv8. Instead, the `dfl` (Distribution Focal Loss) hyperparameter is used, which is related to the fine-grained localization of bounding boxes.

Here’s how you can set these in your custom configuration file:

# Example custom configuration
box: 7.5  # Weight for the GIoU loss component
cls: 0.5  # Weight for the classification loss
dfl: 1.5  # Weight for the distribution focal loss

To summarize:

* `box` corresponds to the GIoU loss.

* `cls` corresponds to the classification loss.

* `dfl` is related to the localization aspect, which can be considered as part of the bounding box refinement.

If you have any further questions or need additional assistance, feel free to ask. Happy training! 🚀

Hi @Vayne0227,

Absolutely, I'd be happy to clarify that for you! In the context of YOLOv8, the weight coefficients for the different loss components are as follows:

* **Box Loss (GIoU Loss)**: This is controlled by the `box` hyperparameter. It determines the weight of the bounding box regression loss, which typically uses GIoU (Generalized Intersection over Union).

* **Classification Loss**: This is controlled by the `cls` hyperparameter. It sets the weight for the classification loss, which is responsible for correctly classifying the detected objects.

* **L1 Loss**: This is not directly specified as a separate hyperparameter in YOLOv8. Instead, the `dfl` (Distribution Focal Loss) hyperparameter is used, which is related to the fine-grained localization of bounding boxes.

Here’s how you can set these in your custom configuration file:

# Example custom configuration
box: 7.5  # Weight for the GIoU loss component
cls: 0.5  # Weight for the classification loss
dfl: 1.5  # Weight for the distribution focal loss

To summarize:

* `box` corresponds to the GIoU loss.

* `cls` corresponds to the classification loss.

* `dfl` is related to the localization aspect, which can be considered as part of the bounding box refinement.

If you have any further questions or need additional assistance, feel free to ask. Happy training! 🚀

Hi, I have a question. Why box=7.5， cls= 0.5 ，dfl=1.5 ，Is there any connection between them，For example, three numbers add up to 9.5, but this is just an example，Or is there a proportional relationship between these three numbers。 I've trained my own model now, and the detection confidence is a little low，I want to give more weight to cls，Just simple box=7.5， cls= 1.0 ，dfl=1.5?

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