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.👍

[vorzee4]

Hello @glenn-jocher , I also want to ask. I trained my model for detection task and the box loss value of my model is 1.042, but, if my knowledge not mistaken, CIoU loss value should be between -1 to 1. Is that possible? can you give explanation.

[glenn-jocher]

Hello @vorzee4,

Thank you for your question! It's great to see your engagement with the YOLOv8 model.

Regarding your observation, the CIoU (Complete Intersection over Union) loss indeed typically ranges between 0 and 1 for individual bounding boxes. However, the box_loss value you see during training is an aggregate measure, often a sum or mean over all bounding boxes in a batch. This can result in values greater than 1, especially when summed over many instances.

Here's a brief explanation:

• CIoU Loss: Measures the overlap between predicted and ground truth boxes, considering aspect ratio and distance between box centers.
• Box Loss: In training logs, this is usually the sum of CIoU losses over all bounding boxes in a batch, hence it can exceed 1.

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

[vorzee4]

Are cls loss and dfl loss also logged with sum value of every object detected loss in a batch? and what are their range value for single detection for cls loss (varifocal loss) and dfl loss?

[glenn-jocher]

Hello @vorzee4,

Great question! Yes, both cls_loss (classification loss) and dfl_loss (distribution focal loss) are typically logged as the sum over all detected objects in a batch, similar to the box_loss. This means their logged values can exceed 1, especially with larger batch sizes.

For a single detection:

• Cls Loss (Varifocal Loss): This loss measures the classification error and can vary widely depending on the confidence scores and the number of classes. It doesn't have a strict range but is generally between 0 and 1 for individual detections.
• Dfl Loss (Distribution Focal Loss): This loss focuses on the localization precision of bounding boxes. Like the CIoU loss, its value for a single detection is typically between 0 and 1.

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

[vorzee4]

After all the loss values ​​are added up, will they also be added up with the loss values ​​from other batches and then divided by the number of batches and get the average value of loss in a batch and that is the value that is logged?

[glenn-jocher]

Hello!

Yes, the loss values you see logged during training are typically averaged over the entire batch. Here's a quick breakdown:

1. Per Batch Calculation: For each batch, individual losses (e.g., box_loss, cls_loss, dfl_loss) are summed up for all detections within that batch.
2. Averaging: These summed losses are then averaged over the number of batches to provide a mean loss value, which is what gets logged.

This averaging helps in stabilizing the training process and provides a clearer picture of the model's performance over time.

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

For more detailed information, you can refer to our documentation.

[vorzee4]

So, if I have this box loss of 3 models that differentiated by its batch size, I can say that batch size 32 have the lowest loss per image because in single batch there are 32 images, so the mean loss for an image is calculated using logged box loss value divide by 32, while other models divided by smaller number. Is this true?

[pderrenger]

Yes, that's correct! The logged box loss value is typically the sum of losses for all images in the batch. So, for a batch size of 32, the mean loss per image would indeed be the logged box loss divided by 32. This means a lower logged loss for a larger batch size generally indicates a lower mean loss per image. If you have further questions, feel free to ask! 😊

[fraborg99]

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. 👍

HI! Just one quick question about the loss composition. You mentioned that in the final loss, for what reagrds the classification, the varifocal loss is used (which is perfect for me as I have a highly unbalanced dataset). However if I look at the source code, the lines in which the varifocal loss should be called are commented out and instead BCEWithLogitsLoss is used.

[pderrenger]

Hi!

You're correct in your observation. While the Varifocal Loss (VFL) is designed to handle class imbalances effectively, the current implementation in YOLOv8 uses BCEWithLogitsLoss for classification loss (cls_loss). The VFL lines are indeed commented out in the source code. This choice is likely due to practical considerations and performance trade-offs observed during development. If you have a highly unbalanced dataset, you might consider experimenting with different loss functions or adjusting class weights to see what works best for your specific case. If you encounter any issues or have further questions, feel free to ask!

[Mikael1226]

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[Mikael1226]

Hello @glenn-jocher , i am training my dataset on yolov8 models for object detection. then i got the results but i didn't understand why the losses are greater than 1 at the begining of the epochs ? please can you give me the link of the appropriate sources that explain this reason because i am writing a report for my internship ?

[glenn-jocher]

Hello @Mikael1226,

It's normal for losses to be greater than 1 at the beginning of training as the model starts with random weights and gradually learns to minimize the loss. For detailed explanations, please refer to the Ultralytics YOLO documentation.

[AshishreddyM26]

Hello, Thanks for the information regarding loss metrics used during training

Can we get the validation box_loss at every epoch during training to make sure the model doesn't overfit? If yes, how can we access it (through any variable) ?

Why I choose the validation box_loss is to do early stopping, Note: I have only one class so looking to make sure the model detects the objects accurately.

Thank you Ashish

[glenn-jocher]

Hello Ashish,

Yes, you can monitor validation box_loss during training. Use the val method after each epoch to evaluate the model on your validation set. This will help you track overfitting and implement early stopping based on validation performance.

[AshishreddyM26]

Thanks for the response Glenn,

Can you provide some more information about the approach.

I have few doubts regarding this,

1. I would like to use the val/box_loss as the criteria for early stopping, from the above conversations, I learnt that the mAP has been using as the criteria for early stopping. Is there a way to get access for the val/box_loss to be considered instead of mAP for early stopping?

2. Can I perform few calculation for every iteration during training of the detection model ? Because, as soon as I start training, there is nothing in control, I mean the model is training for all the epochs at once, is there a way to manipulate it, because the results.csv has the val/box_loss for every iteration, from this I can do some comparisons for early stopping. I don't know how to approach this.

Am I missing on something ?

Thank you, Ashish

[glenn-jocher]

Hi Ashish,

To use val/box_loss for early stopping, you can modify the training loop to monitor this metric and implement a custom early stopping mechanism. For calculations during each iteration, you might need to adjust the training script to include your logic. If you're using the Ultralytics framework, consider customizing the code to fit your needs.

[kundetiA]

Hello @glenn-jocher,

I have looked at the code of Loss.

In the YOLOv8 Detection task, loss is cls_loss + box_loss + dfl_loss.

In cls_loss, BCEWithLogitsLoss is taken instead of Varifocal loss.

Here is the Code i have found:

loss[1] = self.varifocal_loss(pred_scores, target_scores, target_labels) / target_scores_sum # VFL way commented out loss[1] = self.bce(pred_scores, target_scores.to(dtype)).sum() / target_scores_sum # BCE

vfl loss is commented out.

Please provide a information on this.

Thank you kundeti

[glenn-jocher]

Hello Kundeti,

In YOLOv8, BCEWithLogitsLoss is currently used for classification loss instead of Varifocal Loss. This choice is made for stability and performance reasons. If you need further customization, you might consider modifying the code to suit your specific requirements.

[glenn-jocher]

@fraborg99 hi! You're correct. While the code includes Varifocal Loss, the default implementation uses BCEWithLogitsLoss. You can modify the code to use Varifocal Loss if it suits your dataset better.

[dxdiag-123]

Hello, I'd like to ask some questions regarding YOLOv8 loss function for detection task.

1. Is the final loss formula for YOLOv8 object detection equal to box_loss + cls_loss + dfl_loss as commented by kundetiA, or is it something else? @glenn-jocher didn't comment about this part so I'm not very sure.
2. I have done a fine-tuning of YOLOv8 model, and I obtained val/cls_loss and val/box_loss as followed. I found from the result that the losses have high value at the start, and this seemed to be a normal behaviour according to previous comments. However, what is the expected final values for these losses? From what I understand, losses should decrease until they plateaued close to zero, but my cls_loss and box_loss plateaued at 1.01 and 1.4 respectively.

Thanks in advance.

[ngocnh22]

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. 👍

Hello! Can you break down the formula of bbx loss? I have a question, in YOLOV8 with object detection actually the model typically uses IoU or CIoU, I am confused about this. Thank you for being so helpful!

[glenn-jocher]

Hello! In YOLOv8, the box_loss typically uses CIoU (Complete Intersection over Union) for bounding box regression to enhance localization accuracy. If you have further questions, feel free to ask!