Confusion Matrix

[hsaine]

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Question

Hello, I am working on a fence detection project with two classes: "fence" and "fence anomaly". Additionally, there is a third class, "background", which appears by default in my model. I want to enhance the performance of the model and I am curious about the expected percentage range for the "background" class. Is it normal for the "background" class to appear or not?

I already augmented the data with different operations and labeled the entire images using bounding boxes. Which parameters should I modify? I played with epoch time, batch size, and optimizer, but I didn't see any results.

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[glenn-jocher]

@hsaine hello! 😊

It's great to hear about your project on fence detection. Regarding your question about the "background" class, it's quite common for object detection models like YOLOv5 to include a "background" class implicitly, especially to help the model distinguish between the foreground classes ("fence" and "fence anomaly") and other irrelevant parts of the image.

If you're looking to improve model performance and have already experimented with data augmentation, epochs, batch size, and optimizers without success, you might consider adjusting the learning rate or experimenting with different anchor box sizes which can be crucial for detecting objects of varying dimensions accurately.

Additionally, reviewing the loss components (classification, objectness, and box regression) during training might provide insights into what aspect of the learning process needs more tuning.

Keep up the good work, and don't hesitate to reach out if you have more questions! 🚀

[hsaine]

thank you for your response, Is it normal if it appears in high percentage in the matrix?

[glenn-jocher]

Hello @hsaine! 👋

Yes, it's fairly normal for the "background" class to appear in a high percentage in the confusion matrix, especially if a significant portion of your images consists of areas without the target objects ("fence" and "fence anomaly"). This indicates that your model is learning to identify non-target areas effectively, which is crucial for reducing false positives.

If the high percentage is a concern, consider checking if the bounding boxes in your training data are accurately and tightly defined around your target objects. Mislabeling or overly generous bounding boxes can lead to higher background predictions.

Keep up the good work! 😊

[hsaine]

Hello,

I am so sorry if I disrupt you. Now I am concentrating only on fence anomalies labeled as broken. If I want to decrease the false negatives, what should I do?
Thank you!

[glenn-jocher]

Hello @hsaine,

No disruption at all! 😊 To decrease false negatives for your "broken fence" class, consider the following strategies:

1. Data Augmentation: Increase the variety and quantity of your training data with augmentations like rotations, flips, and brightness adjustments.
2. Class Imbalance: Ensure your dataset has a balanced representation of the "broken fence" class.
3. Hyperparameter Tuning: Experiment with different learning rates, batch sizes, and epochs.
4. Anchor Boxes: Adjust anchor box sizes to better fit the dimensions of your "broken fence" instances.
5. Model Architecture: Try different YOLOv5 models (e.g., YOLOv5s, YOLOv5m) to see which performs best.

Feel free to reach out if you have more questions. Good luck with your project! 🚀

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