Confusion Matrix wrong output about yolov5 HOT 2 OPEN

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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

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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

from yolov5.

Hello! Thanks for reaching out with your inquiry about the confusion matrix output 😊.

It looks like there might be a mismatch between your validation data labeling and the predictions during the evaluation. Here’s a short checklist to help you debug the issue:

1. Verify Labels: Double-check your background validation images to ensure they are correctly labeled with no objects.
2. Model Check: Ensure the model weights used during validation are indeed the ones intended and correspond to the correct training checkpoint.
3. Evaluation Script: Review the evaluation script, specifically how the confusion matrix is populated, to confirm it correctly handles cases with no detections.

If everything seems correct and the issue persists, you might want to adjust the confidence threshold temporarily to see if low-confidence detections are inadvertently affecting the matrix.

Feel free to follow up if the problem remains unresolved! 😊

from yolov5.