How to adjust effect of loss functions for yolov5 training ?

Grhanas commented 1 month ago

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Hello, I want to learn how can i change the effects of loss functions for training. For example dfl_loss is more important for me than other loss functions. How can i make more important this loss function for my training.

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UltralyticsAssistant commented 1 month ago

👋 Hello @Grhanas, thank you for your interest in YOLOv5 🚀!

It looks like you want to adjust the influence of specific loss functions during training. Please visit our ⭐️ Tutorials for a comprehensive guide on training and customization techniques. While you're there, you might find the Custom Data Training and Hyperparameter Evolution sections particularly useful.

If this question relates to a 🐛 Bug Report, please ensure you provide a minimum reproducible example to help us debug further.

For custom training ❓ Questions, please provide detailed information, including dataset examples and training logs, and review our Tips for Best Training Results.

Requirements

Ensure you have Python>=3.8.0 with all dependencies from requirements.txt installed, including PyTorch>=1.8. You can get started with:

git clone https://github.com/ultralytics/yolov5
cd yolov5
pip install -r requirements.txt

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pderrenger commented 1 week ago

@Grhanas to adjust the effect of loss functions in YOLOv5, you can modify the loss weights in the train.py script or the relevant configuration files. Look for parameters like cls, obj, and box in the hyperparameter settings and adjust them to prioritize specific losses. For more detailed guidance, refer to the YOLOv5 documentation on hyperparameter tuning.

ultralytics / yolov5