Confused about interpreting Loss plots about yolov5

[github-actions]

👋 Hello @ashwin-999, thank you for your interest in YOLOv5 🚀! Please visit our ⭐️ Tutorials to get started, where you can find quickstart guides for simple tasks like Custom Data Training all the way to advanced concepts like Hyperparameter Evolution.

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Python>=3.8.0 with all requirements.txt installed including PyTorch>=1.8. To get started:

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

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Designed to be fast, accurate, and easy to use, YOLOv8 is an ideal choice for a wide range of object detection, image segmentation and image classification tasks. With YOLOv8, you'll be able to quickly and accurately detect objects in real-time, streamline your workflows, and achieve new levels of accuracy in your projects.

Check out our YOLOv8 Docs for details and get started with:

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[glenn-jocher]

@ashwin-999 hello! It's great to see you're diving deep into training custom datasets with YOLOv5. 🚀

The phenomenon you're observing, where validation loss increases while training loss decreases, is often a sign of overfitting. This means your model is learning the training data very well but failing to generalize to even slightly different data (in this case, your validation subset).

Given that your validation set is a subset of your training data, it's unusual but not impossible to see this behavior. It could be due to several factors:

1. High Weight Decay: A weight decay of 1 is quite high and might be causing the optimizer to overly penalize the weights, affecting generalization.
2. Learning Rate: A low learning rate like 0.0001 with AdamW might be too conservative, leading to slow or suboptimal learning paths.
3. Data Distribution: Ensure the validation subset is representative of the overall training set. Even if it's a subset, any slight distribution differences can impact performance.

To address this, consider experimenting with:

• Lowering the weight decay to see if it allows for better generalization.
• Adjusting the learning rate or trying a different optimizer.
• Ensuring your validation set is truly representative of the training set's diversity.

Remember, training deep learning models is often an iterative process requiring experimentation with hyperparameters. Don't hesitate to try different configurations to find what works best for your specific dataset.

For more detailed guidance on training and troubleshooting, our documentation might offer additional insights: https://docs.ultralytics.com/yolov5/.

Keep experimenting, and good luck with your project!

[github-actions]

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