background class

[pratikshac15]

Search before asking

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

Question

@glenn-jocher Hello Glenn, I am not able to understand the background class and how it is calculated. Could you please explain more about the background class? also while calculating FN, FP, and TN from the confusion matrix do I need to consider background class? Your reply would be appreciated. thank you in advance.

Additional

No response

[github-actions[bot]]

👋 Hello @pratikshac15, 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.

If this is a 🐛 Bug Report, please provide a minimum reproducible example to help us debug it.

If this is a custom training ❓ Question, please provide as much information as possible, including dataset image examples and training logs, and verify you are following our Tips for Best Training Results.

Requirements

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

Environments

YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled):

• Notebooks with free GPU:
• Google Cloud Deep Learning VM. See GCP Quickstart Guide
• Amazon Deep Learning AMI. See AWS Quickstart Guide
• Docker Image. See Docker Quickstart Guide

Status

If this badge is green, all YOLOv5 GitHub Actions Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 training, validation, inference, export and benchmarks on macOS, Windows, and Ubuntu every 24 hours and on every commit.

Introducing YOLOv8 🚀

We're excited to announce the launch of our latest state-of-the-art (SOTA) object detection model for 2023 - YOLOv8 🚀!

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:

pip install ultralytics

[glenn-jocher]

@pratikshac15 hi there! The "background" class in YOLOv5 refers to the class representing areas of an image that don't contain any specific object of interest. When calculating FN, FP, and TN from the confusion matrix, you typically don't need to consider the background class separately, as it's often treated as a reference point for the absence of other classes. For more details, you can also refer to our documentation at https://docs.ultralytics.com/yolov5/. Hope this helps!

[pratikshac15]

Thank you, Glenn. @glenn-jocher I am curious as you can see in my confusion matrix I am getting an accuracy of 0.98, 0.97, and 1.00 for each class then how come 0.67 and 0.33 are coming in the background for foam and fragment? I couldn't understand the calculations..

[glenn-jocher]

@pratikshac15 You're welcome! The accuracy for each class and the background class is calculated based on several factors including true positives, false positives, and true negatives. The lower accuracy for the background class for foam and fragment might be due to the distribution of predictions and may also be influenced by the specific dataset characteristics. If you have more questions about the calculations, do let me know! 🙌

[pratikshac15]

@glenn-jocher Thank you very very much Glenn for your kind help. Yes, I would like to understand the calculations behind background class accuracy is crucial. Could you elaborate on how the distribution of predictions specifically impacts the accuracy for the background class of foam and fragment? Additionally, regarding dataset characteristics, what specific traits or features might influence this lower accuracy? Delving deeper into these aspects would be helpful to grasp the nuances of the accuracy calculations. The dataset displays distinct characteristics among film, foam, and fragment. Film appears notably thin and transparent, while foam exhibits pores, and fragment presents as solid and white in structure.

[glenn-jocher]

@pratikshac15 You're welcome! The accuracy for the background class in YOLOv5 is indeed influenced by the distribution of predictions and dataset characteristics. In your case, the distinct traits of film, foam, and fragment could impact the background class accuracy. For example, the thin and transparent nature of film, the porous texture of foam, and the solid white structure of fragments might cause certain classes to be misclassified as the background. Additionally, if the distribution of training examples for these classes is imbalanced, it could lead to lower accuracy for the background class of foam and fragment. Understanding these nuances can be helpful for performance optimization. 📊

[pratikshac15]

Thank you very much Glenn @glenn-jocher for all the information and kind help. I appreciate it.

[glenn-jocher]

You're very welcome, @pratikshac15! Always happy to help. If you have more questions in the future, feel free to ask. Good luck with your project! 🚀

[github-actions[bot]]

👋 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 ⭐

[shrutichakraborty]

@pratikshac15 You're welcome! The accuracy for the background class in YOLOv5 is indeed influenced by the distribution of predictions and dataset characteristics. In your case, the distinct traits of film, foam, and fragment could impact the background class accuracy. For example, the thin and transparent nature of film, the porous texture of foam, and the solid white structure of fragments might cause certain classes to be misclassified as the background. Additionally, if the distribution of training examples for these classes is imbalanced, it could lead to lower accuracy for the background class of foam and fragment. Understanding these nuances can be helpful for performance optimization. 📊

Hi! Is there a way to adjust the weights attributed to the background class and the other classes during the training?