How to prevent model from mistaking the background as an object?

[edehino]

Search before asking

• I have searched the YOLOv5 issues and discussions and found no similar questions.

Question

Hi, I know this has been asked before. I have prepared a dataset with two object of interest and trained it using the default parameters. The resulting mAP is good but when I look at the confusion matrix, it shows that 97% of background is predicted as as my object #1.

I tried adding more images with no labels to the training and even the validation set but the result is still the same. Even used yolov5x as a weight but still no improvement.

My dataset is very complex, like in a single photo there are maximum of 200 instances of objects.

Train, test, and val sets are randomly distributed but I failed to come up with a good result.

Anyone who can be of help would be very much appreciated.

Additional

No response

[glenn-jocher]

@edehino hi,

Thank you for reaching out. The issue you're facing with the model mistaking the background as an object can be quite common, especially in complex datasets with multiple object instances.

To improve the model's performance and minimize background mistakes, here are a few suggestions:

1. Data augmentation: Consider applying various data augmentation techniques to your dataset, such as random cropping, rotation, scaling, or flipping. This helps increase the diversity of your training data and encourages the model to learn more robust features.

2. Balance your dataset: Ensure that your dataset is well-balanced, meaning that each class has a similar number of samples. If there are disproportionately more background samples compared to your object classes, the model might focus more on predicting the background. Consider balancing your dataset or adjusting the loss function accordingly to give equal importance to all classes.

3. Collect more diverse data: If possible, try collecting more diverse images that cover a wider range of backgrounds and variations in lighting, perspectives, and object arrangements. This can help train the model to be more adaptable to different scenarios.

4. Experiment with network architecture: You mentioned that you tried using the yolov5x weight, but it might be worth experimenting with different network architectures or configurations. Some architectures might perform better for your particular dataset, so it's worth exploring other options.

5. Hyperparameter tuning: Experiment with different hyperparameters, such as learning rate, batch size, optimizer, and anchor sizes. Fine-tuning these parameters can significantly impact the model's performance.

Lastly, remember that YOLOv5 is maintained by the Ultralytics team, and we constantly rely on contributions, feedback, and support from the community. If you have any further questions or insights, please don't hesitate to engage with the broader community in the discussions section or search through the existing issues.

Hope this helps, and best of luck with your project!

Ultralytics YOLOv5 team

[github-actions]

👋 Hello there! We wanted to give you a friendly reminder that this issue has not had any recent activity and may be closed soon, but don't worry - you can always reopen it if needed. If you still have any questions or concerns, please feel free to let us know how we can help.

For additional resources and information, please see the links below:

• Docs: https://docs.ultralytics.com
• HUB: https://hub.ultralytics.com
• Community: https://community.ultralytics.com

Feel free to inform us of any other issues you discover or feature requests that come to mind in the future. Pull Requests (PRs) are also always welcomed!

Thank you for your contributions to YOLO 🚀 and Vision AI ⭐