Obb loss value problem about ultralytics

[glenn-jocher]

@1005183361 hello,

Thank you for reaching out and providing details about the issue you're experiencing with the OBB rotating box detection model. To better assist you, could you please provide a minimum reproducible code example? This will help us understand the context and reproduce the issue on our end. You can find guidelines for creating a minimum reproducible example here. 📝

Additionally, please ensure that you are using the latest versions of torch and ultralytics. You can update your packages using the following commands:

pip install --upgrade torch ultralytics

Here are a few suggestions that might help improve the convergence of your losses:

1. Data Quality and Augmentation: Ensure that your dataset is well-annotated and balanced. Sometimes, data augmentation techniques can help improve model robustness.

2. Hyperparameter Tuning: While you have already tried adjusting the learning rate and weight decay, consider experimenting with other hyperparameters such as batch size and momentum.

3. Learning Rate Scheduler: Using a learning rate scheduler can help in adjusting the learning rate dynamically during training. For example, you can use ReduceLROnPlateau to reduce the learning rate when a metric has stopped improving.

4. Model Architecture: Sometimes, tweaking the model architecture can help. You might want to experiment with different backbone networks or modify the existing layers.

5. Loss Function: Ensure that the loss function is appropriate for your specific task. Customizing the loss function to better suit the characteristics of your data might yield better results.

Here is a basic example of how you might set up a learning rate scheduler in your training script:

from torch.optim.lr_scheduler import ReduceLROnPlateau

# Assuming 'optimizer' is already defined
scheduler = ReduceLROnPlateau(optimizer, mode='min', factor=0.1, patience=10, verbose=True)

for epoch in range(num_epochs):
    train_one_epoch()  # Your training function
    val_loss = validate()  # Your validation function
    scheduler.step(val_loss)

If you continue to face issues, please share more details about your dataset and training configuration. This will help us provide more targeted advice.

Looking forward to your response!