Loss function explanation

[AlvinBimo23]

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I would like to ask for some basic question for YOLOv8, right now i'm working for my paper using YOLOv8. And correct me if i'm wrong, i'm getting know that YOLOv8 using 3 loss function which are VFL loss, DFL loss, and CIoU loss. But, when i try to train the model i'm getting box_loss, cls_loss, and dfl_loss. I already tried to read some documentaion regarding the loss function, but i'm still kinda confused. Please help me to understand it

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[glenn-jocher]

Hey there! 😊 Your understanding is mostly correct. YOLOv8 does indeed utilize advanced loss functions to optimize the model, including:

• CIoU loss for bounding box regression to improve localization accuracy, represented as box_loss during training.
• DFL loss (Distribution Focal Loss), which you've rightly identified and is directly reported as dfl_loss. It helps the model to better estimate object categories.
• VFL loss (Varifocal Loss), which is not separately shown but is incorporated within cls_loss (class loss) in the training logs. VFL is designed to address imbalances and uncertainties in classification tasks.

Each of these loss components plays a vital role in honing the model's accuracy, each focusing on a different aspect of the detection task (localization, classification, etc.).

Training logs display these as box_loss, cls_loss, and dfl_loss, corresponding to how well the model is performing in respective areas. The goal during training is to minimize these losses for better model performance.

Hope this clears up the confusion! Keep pushing forward with your paper. 👍

[Rakib-Ul-Haque]

what about the model.val(). How to get " box_loss, cls_loss, and dfl_loss" from model.val().

[glenn-jocher]

Hello! During the validation phase with YOLOv8 and model.val(), only key performance metrics like accuracy or mAP (mean Average Precision) are typically evaluated and reported, not individual loss components like box_loss, cls_loss, and dfl_loss. These losses are primarily used during the training phase (model.train()) to optimize the model.

If you're interested in tracking these losses during validation, you would need to modify the underlying code to calculate and log these values specifically during validation, similar to what is done during training.

Happy coding! 😊

[sahithi-kukkala]

Hi, I would like to ask some questions about weights of the loss functions in yolov8. 1) what is the significance of weights of different loss functions in yolov8? like box=7.5 cls=0.5 dfl=1.5 2) how to find and plot the overall loss for every epoch while training model? 3) if I want to change the weights of the losses, what should I keep in mind? I am using yolov8 for layout detection, a problem similar to M6 doc.

[glenn-jocher]

Hi there! Great questions. I'll provide concise answers to help guide you through your YOLOv8 usage:

1) Significance of Weights in Loss Functions: The weights (box, cls, dfl) dictate the emphasis the model puts on each component during training. For instance, box=7.5 puts substantial focus on getting the bounding box coordinates correct, while cls=0.5 and dfl=1.5 adjust the importance of class prediction accuracy and distribution of focal loss, respectively.

2) Plotting Overall Loss: During training, the overall loss is typically logged automatically to your console or tensorboard. If you want to create a custom plot:

   from matplotlib import pyplot as plt

   # Assuming 'results' is a list of loss values per epoch
   plt.plot(results)
   plt.title('Training Loss per Epoch')
   plt.xlabel('Epoch')
   plt.ylabel('Loss')
   plt.show()

3) Changing Loss Weights: When adjusting weights, ensure the new values suit the specifics of your task, like layout detection. Higher weights will prioritize that aspect more during training. Start with small modifications from the default values and observe their impact on validation metrics. This iterative approach helps in identifying the optimal balance.

Hope this helps with your layout detection project using YOLOv8! 🚀

[sahithi-kukkala]

hi, thanks for the answers. 1) I want to know how they got the default weights like 7.5, 0.5,1.5 and when I change those do I have to put any kind of constraint on them? 2) does the overall loss of model mean the weighted sum of the cls loss, dfl loss and box losses ? 3) I don't know if this is right but are they using this weighted sum loss while backpropogation?

[AlvinBimo23]

I also would like to ask another question. I notice that the value of the loss function from the result of training the YOLOv8 model can be above 1, while if i remember it correctly the value of loss should be in between 0-1. Is there any reason why it can be above 1 ?

[glenn-jocher]

Hello! Great question! 😊 The loss values in neural networks, including those from YOLOv8, can indeed be greater than 1. This is common, especially during the early phases of training or with complex loss functions. The range of possible loss values isn't strictly limited to between 0 and 1; it depends on the specific implementation and components of the loss function being used (like sum of squared errors or cross-entropy losses which can exceed 1).

The key point during training is to see these loss values decreasing over time as the model learns and optimizes its parameters. Hope this helps clarify your query! Keep exploring and happy modeling!

[sahithi-kukkala]

hello, I am using yolov8m.pt for finetuning custom dataset, for layout detection task. for this task can I change the weights of kobj loss and pose loss to '0'? box=5, cls=5, dfl=3, pose=0, kobj=0, when I change the weights like this after 10 epochs my training loss is increasing, and then becoming 'Nan'. what might be the issue?

[glenn-jocher]

Hello! When adjusting the loss weights, setting pose=0 and kobj=0 effectively tells the model to ignore the keypoints (if any) and objectness in keypoints during training. This might be causing instability, especially if your model structure or data somehow still depends on these aspects.

The increasing training loss eventually leading to 'Nan' typically indicates a numerical stability issue, possibly from the new loss weighting. Here are a few quick tips:

• Ensure that the rest of your model and data pipeline is compatible with these weights being zero.
• Double-check other hyperparameters and consider using a lower learning rate which can sometimes help prevent NaN losses.
• Gradually adjust weights rather than directly jumping to zero to observe how the model reacts to lesser emphasis on those components.

If problems persist, consider recalibrating the weights more conservatively. Happy fine-tuning! 😊

[bhavyajoshi-mahindra]

hi, thanks for the answers.

1. I want to know how they got the default weights like 7.5, 0.5,1.5 and when I change those do I have to put any kind of constraint on them?
2. does the overall loss of model mean the weighted sum of the cls loss, dfl loss and box losses ?
3. I don't know if this is right but are they using this weighted sum loss while backpropogation?

I am still trying to understand the default values of box, cls and dfl loss which are 7.5, 0.5, 1.5 and how do I manipulate these values to improve the overall accuracy of the model. For example, if I have un-balanced classes, then by what factor should I change the value of dfl or cls loss, can it be around 3, 1 or 5, 5 or something else . Also, I am wondering what will be the effect on training if I just increase them all by a multiplication of 5 or 10? Overall I am trying to understand how (or by what value) to change these hyperparameters to improve the accuracy of the result?

Is there a trade-off between these 3 losses, like increasing the value of one might affect the loss calculation of other?

[glenn-jocher]

Hi there! 😊

The default weights for the loss functions (7.5 for box, 0.5 for cls, and 1.5 for dfl) were determined through extensive experimentation and tuning to balance the contributions of each component to the overall loss effectively. You can indeed adjust these weights:

1. Manipulating Loss Weights: If you're dealing with unbalanced classes, increasing the cls (class loss) might help. For example, trying values like cls=1 or cls=2 could proportionally increase the penalty for misclassifications, which may help correct class imbalance issues.

2. Effects of Scaling Loss Weights: Increasing all losses by a common factor (like multiplying by 5 or 10) will not change the learning focus but might affect the convergence rate due to overall scale adjustment of gradients during backpropagation. It's usually more effective to adjust them relative to one another rather than scaling up all equally.

3. Trade-offs: Indeed, there are trade-offs! Increasing one versus the others might make the model focus more on that aspect (e.g., more on getting bounding boxes right than classifying). Balancing this can be crucial depending on what’s more critical for your specific application.

Experimentally adjusting these weights while monitoring validation performance is usually the best strategy to find what works best for your unique dataset.👍