Why does training accuracy suddently decrease? about ultralytics HOT 7 CLOSED

Hi there!

Thank you for providing detailed information about your setup and environment. It looks like you're experiencing a sudden decrease in training accuracy after 200 epochs, which can indeed be indicative of overfitting or other issues. Let's address this step-by-step:

1. Reproducible Example: To better understand and investigate the issue, could you please provide a minimal reproducible code example? This will help us replicate the problem on our end. You can find guidelines on creating a minimal reproducible example here. This is crucial for us to diagnose and solve the issue effectively.

2. Package Versions: Ensure you are using the latest versions of torch and ultralytics. Sometimes, bugs are fixed in newer releases. You can upgrade your packages using the following commands:

   pip install --upgrade torch ultralytics

3. Overfitting: Given that you have a substantial dataset, overfitting might still occur due to various reasons such as model complexity or insufficient regularization. Here are a few strategies to mitigate overfitting:

   • Early Stopping: Implement early stopping to halt training when the validation performance starts to degrade.
   • Data Augmentation: Increase data augmentation to introduce more variability in your training data.
   • Regularization: Add regularization techniques such as dropout or weight decay.

4. Hyperparameters: While you mentioned using default hyperparameters, it might be beneficial to experiment with different learning rates, batch sizes, and other hyperparameters. Sometimes, the optimal settings can vary significantly between different datasets and training runs.

5. Monitoring Metrics: Continuously monitor not just the loss but also other metrics like precision, recall, and mAP. Tools like TensorBoard or wandb can be very helpful for this.

Here’s a quick example of how you might set up early stopping and data augmentation in your training script:

from ultralytics import YOLO

# Load a model
model = YOLO('yolov8n.pt')

# Train the model with early stopping and data augmentation
results = model.train(
    data='/path/to/your/data.yaml',
    epochs=300,
    patience=20,  # Early stopping patience
    augment=True  # Enable data augmentation
)

Feel free to share any additional details or questions you might have. We're here to help! 😊

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@glenn-jocher Hi and thanks for your reply.
Do I understand correctly that I have to pass augment=True, otherwise YOLOv8 does not perform any augmentation at all?
I don't find anything about this parameter in the training docs.
Additionally, these docs state that "dropout" is only used for classification tasks - but I am training an obj. detection task.

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Hi @haimat,

Thank you for your follow-up! 😊

To clarify, YOLOv8 does indeed perform data augmentation by default during training, even if you don't explicitly set augment=True. The augment parameter is there to give you control over the augmentation process, allowing you to enable or disable it as needed. If you want to customize the augmentation settings further, you can modify the augmentation parameters directly in the training configuration.

Regarding the "dropout" parameter, you are correct that it is primarily used for classification tasks. For object detection tasks, dropout is not typically applied. Instead, other regularization techniques and data augmentation strategies are more commonly used to improve model generalization and prevent overfitting.

If you have any more questions or need further assistance, feel free to ask. We're here to help! 🚀

from ultralytics.

Well, beside augmentation, what other things can I do during YOLOv8 training to prevent overfitting in an object detection task?

from ultralytics.

Hi @haimat,

Great question! Preventing overfitting is crucial for achieving a robust and generalizable model. Here are several strategies you can employ during YOLOv8 training to mitigate overfitting in your object detection task:

1. Early Stopping: Implement early stopping to halt training when the validation performance starts to degrade. This prevents the model from overfitting to the training data.

   from ultralytics import YOLO
   
   # Load a model
   model = YOLO('yolov8n.pt')
   
   # Train the model with early stopping
   results = model.train(
       data='/path/to/your/data.yaml',
       epochs=300,
       patience=20  # Early stopping patience
   )

2. Data Augmentation: While YOLOv8 performs data augmentation by default, you can customize the augmentation settings to introduce more variability in your training data. This helps the model generalize better.

3. Regularization Techniques: Although dropout is primarily used for classification tasks, you can still apply other regularization techniques such as weight decay (L2 regularization) to your model.

   # Example of setting weight decay
   results = model.train(
       data='/path/to/your/data.yaml',
       epochs=300,
       weight_decay=0.0005  # L2 regularization
   )

4. Learning Rate Scheduling: Use learning rate scheduling to adjust the learning rate during training. This can help the model converge more effectively and avoid overfitting.

   results = model.train(
       data='/path/to/your/data.yaml',
       epochs=300,
       lr_scheduler='cosine'  # Example of cosine annealing scheduler
   )

5. Increase Dataset Size: If possible, increase the size of your dataset. More data can help the model learn better and generalize well to unseen data.

6. Cross-Validation: Use cross-validation to ensure that your model's performance is consistent across different subsets of your data. This can help identify if the model is overfitting to a particular subset.

7. Monitor Metrics: Continuously monitor not just the loss but also other metrics like precision, recall, and mAP. Tools like TensorBoard or wandb can be very helpful for this.

If you haven't already, please ensure you're using the latest versions of torch and ultralytics to benefit from the latest features and bug fixes. You can upgrade your packages using the following commands:

pip install --upgrade torch ultralytics

If you encounter any issues, please provide a minimal reproducible code example so we can investigate further. You can find guidelines on creating a minimal reproducible example here.

Feel free to reach out if you have any more questions or need further assistance. We're here to help! 😊

from ultralytics.

Hi, I have started the training again with exactly the same hyperparams, and now everything worked fine :)

from ultralytics.

Hi @haimat,

I'm glad to hear that your training is now running smoothly! 😊 Sometimes, rerunning the training can resolve transient issues that might occur due to various factors like system load or random initialization.

If you encounter any further issues or have more questions, feel free to reach out. We're here to help!

Happy training! 🚀

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