YOLOv8 oriented bounding box losses about ultralytics HOT 5 CLOSED

Hello!

Great question! To refine the orientation accuracy when working with oriented bounding boxes (OBB), you can indeed adjust certain hyperparameters. A common approach is to modify the loss weights in your model's configuration file. Here’s a quick guide:

1. Increase the Rotation Loss Weight: Enhance the weight of the rotation component in the loss function. This gives the model more incentive to correctly predict the orientation.

2. Fine-Tuning: Sometimes, slightly adjusting the learning rate can also help the model to focus more on finer details like orientation.

3. Custom Loss Function: If you're feeling adventurous, you can implement a custom loss function that more heavily penalizes inaccuracies in angle prediction.

Here's a snippet for adjusting the loss weights, assuming you’re using a custom YAML file:

# Example of increasing the orientation loss weight
loss:
  box: 0.05  # bounding box loss weight
  obj: 1.0   # objectness loss weight
  cls: 0.5   # class confidence loss weight
  angle: 2.0 # increase rotation angle loss weight

Remember to experiment with these hyperparameters carefully and monitor the training to ensure the model is not overfitting on the orientation at the expense of other important metrics.

Best of luck with your training! 🚀

from ultralytics.

Hi Glenn,
Thanks for the reply. I found the parameters box, obj, and cls, but didnt find any parameter called 'angle' in the config file. I am modifying the default yolov8 config file. when i tried to pass the 'angle' parameter i got a error saying 'angle' is not a valid YOLO argument. Is there any other way to include angle loss into the config.

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I had two yaml files - one for config (which had train, augmentation and other configs), another for giving the paths of dataset and names of classes. I added the loss: \n angle: parameter into the path config yaml file, and it started training, but I cant find if it did actually train with those parameter. No errors were thrown so I assume this is correct?

from ultralytics.

Hello!

It sounds like you've made the right adjustments by adding the angle parameter to your YAML configuration. If no errors were thrown during training, it's likely that the parameter was accepted. However, to confirm that the angle parameter is actively influencing the training, you should monitor the training logs for any changes in loss values related to the orientation. Additionally, evaluating the model on a validation set and checking the orientation accuracy can provide more insights into the effectiveness of your modifications.

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

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