Why is background FP so high?

[a41497254]

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Question

I am using my customized data set, and the training result background FP is very high. The following are the relevant training results. What problem did I encounter?

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[github-actions]

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[glenn-jocher]

@a41497254 hello there! 😊 It looks like you're facing an issue with high false positives (FP) for background in your custom dataset. This can be a bit tricky, but let's tackle it together.

A high background FP usually indicates that the model is having difficulty distinguishing between background and foreground classes. This might be due to a few reasons:

1. Imbalanced Data: Ensure your dataset has a balanced number of instances for each class, including diverse backgrounds.
2. Data Quality: Verify that your images and annotations are of high quality. Low resolution or poorly annotated images can confuse the model.
3. Model Complexity: For complex datasets, you might need to adjust the model architecture or increase the depth / width of your model to better capture features.
4. Training Parameters: Experiment with different learning rates, batch sizes, or augmentation techniques. Sometimes tweaking these can lead to significant improvements.

If you've not done it yet, I recommend checking our documentation on best practices for training on custom datasets here: https://docs.ultralytics.com/yolov5/. It's filled with insights that can help improve your model's performance.

Let's make your YOLOv5 journey successful together! And remember, the road to a perfectly trained model might take some experimentation. Stay curious! 🌟

[a41497254]

Hello @glenn-jocher , I want to ask about data imbalance. This is a picture of my data distribution. I have the most "W" types. Why is high background FP the most serious after the model is trained?

[glenn-jocher]

Hello @a41497254! 👋 Data imbalance can indeed cause models, like YOLOv5, to have higher false positives, especially in categories with fewer examples. This happens because the model becomes biased towards the classes with more data—like "W" in your case, and might over-predict them, leading to more background being incorrectly classified.

A few strategies to mitigate this include:

• Data Augmentation: Increase the diversity of your smaller classes through methods such as flipping, scaling, or color variation.
• Weighted Loss: Adjust the loss function to weigh errors on minority classes more than errors on majority classes.
• Oversampling: Manually or automatically oversample the minority classes to balance the dataset.

Remember, every dataset and problem is unique, so it might take some experimentation to find the right approach for your specific case. Happy training! 💪

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