Changing Class Weights In YOLOV7

[eraydikyologlu]

Search before asking

• [X] I have searched the HUB issues and discussions and found no similar questions.

Question

I trained my model on a dataset using Yolov7. But my model did not perform well in some classes. When I examined my data set, I saw that there was very little data in those classes. Instead of methods such as data augmentation, I want my model to give more weight to classes with less data. How can I make this happen?

Additional

No response

[github-actions[bot]]

👋 Hello @eraydikyologlu, thank you for raising an issue about Ultralytics HUB 🚀! Please visit our HUB Docs to learn more:

• Quickstart. Start training and deploying YOLO models with HUB in seconds.
• Datasets: Preparing and Uploading. Learn how to prepare and upload your datasets to HUB in YOLO format.
• Projects: Creating and Managing. Group your models into projects for improved organization.
• Models: Training and Exporting. Train YOLOv5 and YOLOv8 models on your custom datasets and export them to various formats for deployment.
• Integrations. Explore different integration options for your trained models, such as TensorFlow, ONNX, OpenVINO, CoreML, and PaddlePaddle.
• Ultralytics HUB App. Learn about the Ultralytics App for iOS and Android, which allows you to run models directly on your mobile device.
  • iOS. Learn about YOLO CoreML models accelerated on Apple's Neural Engine on iPhones and iPads.
  • Android. Explore TFLite acceleration on mobile devices.
• Inference API. Understand how to use the Inference API for running your trained models in the cloud to generate predictions.

If this is a 🐛 Bug Report, please provide screenshots and steps to reproduce your problem to help us get started working on a fix.

If this is a ❓ Question, please provide as much information as possible, including dataset, model, environment details etc. so that we might provide the most helpful response.

We try to respond to all issues as promptly as possible. Thank you for your patience!

[pderrenger]

@eraydikyologlu hello!

Thank you for reaching out with your question. To address the issue of class imbalance in your dataset, you can indeed adjust the class weights in YOLOv7 to give more importance to the underrepresented classes. This can help improve the model's performance on those classes.

Here's a step-by-step guide on how to adjust class weights in YOLOv7:

1. Modify the Training Script: You need to modify the training script to include class weights. This typically involves adjusting the loss function to account for the weights.

2. Calculate Class Weights: Calculate the weights for each class based on the inverse frequency of the classes in your dataset. For example:

   import numpy as np
   
   # Example class frequencies
   class_counts = np.array([100, 200, 50, 25])  # Replace with your actual class counts
   total_samples = np.sum(class_counts)
   class_weights = total_samples / (len(class_counts) * class_counts)
   
   print("Class Weights:", class_weights)

3. Integrate Class Weights into YOLOv7: Once you have the class weights, you need to integrate them into the YOLOv7 training process. This typically involves modifying the loss function in the training script to include these weights.

Here is a simplified example of how you might modify the loss function:

import torch

# Assuming `class_weights` is a tensor of weights for each class
class_weights = torch.tensor([1.0, 0.5, 2.0, 4.0])  # Replace with your actual class weights

def weighted_loss(pred, target):
    loss = torch.nn.CrossEntropyLoss(weight=class_weights)
    return loss(pred, target)

# Use `weighted_loss` in your training loop

4. Update Configuration: Ensure that your training configuration file reflects any changes you make to the training script and loss function.

By following these steps, you should be able to adjust the class weights and potentially improve the performance of your model on the underrepresented classes.

If you encounter any issues or have further questions, please feel free to ask. Happy training! 😊

[eraydikyologlu]

@eraydikyologlu hello!

Thank you for reaching out with your question. To address the issue of class imbalance in your dataset, you can indeed adjust the class weights in YOLOv7 to give more importance to the underrepresented classes. This can help improve the model's performance on those classes.

Here's a step-by-step guide on how to adjust class weights in YOLOv7:

1. Modify the Training Script: You need to modify the training script to include class weights. This typically involves adjusting the loss function to account for the weights.

2. Calculate Class Weights: Calculate the weights for each class based on the inverse frequency of the classes in your dataset. For example:

   import numpy as np
   
   # Example class frequencies
   class_counts = np.array([100, 200, 50, 25])  # Replace with your actual class counts
   total_samples = np.sum(class_counts)
   class_weights = total_samples / (len(class_counts) * class_counts)
   
   print("Class Weights:", class_weights)

3. Integrate Class Weights into YOLOv7: Once you have the class weights, you need to integrate them into the YOLOv7 training process. This typically involves modifying the loss function in the training script to include these weights.

Here is a simplified example of how you might modify the loss function:

import torch

# Assuming `class_weights` is a tensor of weights for each class
class_weights = torch.tensor([1.0, 0.5, 2.0, 4.0])  # Replace with your actual class weights

def weighted_loss(pred, target):
    loss = torch.nn.CrossEntropyLoss(weight=class_weights)
    return loss(pred, target)

# Use `weighted_loss` in your training loop

4. Update Configuration: Ensure that your training configuration file reflects any changes you make to the training script and loss function.

By following these steps, you should be able to adjust the class weights and potentially improve the performance of your model on the underrepresented classes.

If you encounter any issues or have further questions, please feel free to ask. Happy training! 😊

Thank you for reply. But i have made a few changes in loss.py. Here they are:

class ComputeLoss: def init(self, model, autobalance=False): super(ComputeLoss, self).init() device = next(model.parameters()).device # get model device h = model.hyp # hyperparameters

    # Manually enter the weights
    manual_class_weights = torch.tensor(
        [0.5232, 0.5232, 1.8757, 0.5231, 0.5724, 0.7809, 1.8850, 1.8822, 1.8822, 1.9272, 3.2555, 1.9087, 1.5146, 31.3442, 0.5572, 0.5235, 2.7443],
        device=device
    )

    # Define criteria
    BCEcls = nn.BCEWithLogitsLoss(pos_weight=manual_class_weights)
    BCEobj = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h["obj_pw"]], device=device))

I defined the pos_weight manually and trained the model with these changes. But i am not sure that it detects the under represented class. Why do you think it might be? The number of data in that class is quite low compared to others. Could it be because of this? But I increased its weight to prevent this, but it still couldn't detect it.

[pderrenger]

Hello @eraydikyologlu,

Thank you for sharing the changes you made to loss.py. It looks like you've correctly modified the pos_weight for the BCEWithLogitsLoss to account for class imbalance. However, there are a few additional considerations that might help improve the detection of underrepresented classes:

1. Data Augmentation: While adjusting class weights is a good step, data augmentation can also significantly help in improving the model's performance on underrepresented classes. Techniques like oversampling, synthetic data generation, and augmentation (e.g., rotation, scaling, flipping) can create more diverse training examples for the minority classes.

2. Learning Rate and Training Duration: Ensure that your learning rate is appropriately set. Sometimes, a lower learning rate can help the model learn better, especially for underrepresented classes. Additionally, consider training for more epochs to give the model more opportunities to learn from the minority class examples.

3. Validation and Testing: Make sure that your validation and test sets are balanced and representative of the class distribution. This will help you accurately assess the model's performance on underrepresented classes.

4. Loss Function Adjustment: While you've adjusted the pos_weight, you might also want to experiment with different loss functions or additional regularization techniques to see if they yield better results.

5. Model Architecture: Depending on the complexity of your dataset, you might need to experiment with different model architectures or hyperparameters to find the best fit for your specific use case.

Here's a small snippet to ensure your weights are correctly applied and to double-check your implementation:

import torch.nn as nn

class ComputeLoss:
    def __init__(self, model, autobalance=False):
        super(ComputeLoss, self).__init__()
        device = next(model.parameters()).device  # get model device
        h = model.hyp  # hyperparameters

        # Manually enter the weights
        manual_class_weights = torch.tensor(
            [0.5232, 0.5232, 1.8757, 0.5231, 0.5724, 0.7809, 1.8850, 1.8822, 1.8822, 1.9272, 3.2555, 1.9087, 1.5146, 31.3442, 0.5572, 0.5235, 2.7443],
            device=device
        )

        # Define criteria
        BCEcls = nn.BCEWithLogitsLoss(pos_weight=manual_class_weights)
        BCEobj = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h["obj_pw"]], device=device))

        # Ensure these are used in your training loop
        self.BCEcls = BCEcls
        self.BCEobj = BCEobj

    def __call__(self, p, targets):  # example forward method
        # Compute your losses here using self.BCEcls and self.BCEobj
        pass

If the issue persists, it might be beneficial to share more details about your dataset and training process. This way, the community can provide more targeted advice.

Feel free to reach out if you have any further questions or need additional assistance. Happy training! 😊