The result of val in confusion matrix about ultralytics

[github-actions]

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[glenn-jocher]

Hello!

You're correct in your understanding of the iou and conf parameters relating to the non-maximum suppression (NMS) process, rather than directly affecting the confusion matrix or mAP calculations. These parameters help in filtering out overlapping bounding boxes based on their confidence scores and IoU values during detection.

For modifying the parameters like conf and iou_thres in the confusion matrix calculation, you can indeed adjust these values in the __init__ method of the ConfusionMatrix class within ultralytics/utils/metrics.py. This adjustment will allow you to tailor the confusion matrix calculations to better fit your specific requirements.

Regarding the calculation of mAP using the largest mean F1 score, YOLOv8 typically computes mAP across a range of IoU thresholds (from 0.5 to 0.95) to provide a more comprehensive evaluation metric that balances both precision and recall across these thresholds.

If you have any more questions or need further clarification, feel free to ask. Happy coding! 😊

[zwcity]

@glenn-jocher
Hello, why did v8 choose the default 0.45 as the IOU threshold for the confusion matrix instead of 0.5?

[zwcity]

@glenn-jocher
Hello, I also noticed that there are two approaches used when calculating the predicted results to match the true labels. One is when calculating the map,

match_predictions():
                        ....
                        matches = matches[iou[matches[:, 0], matches[:, 1]].argsort()[::-1]]
                        matches = matches[np.unique(matches[:, 1], return_index=True)[1]]
                        # matches = matches[matches[:, 2].argsort()[::-1]]
                        matches = matches[np.unique(matches[:, 0], return_index=True)[1]]
                        .....

and the other is when calculating the confusion matrix,

process_batch():
                .....
                matches = matches[matches[:, 2].argsort()[::-1]]
                matches = matches[np.unique(matches[:, 1], return_index=True)[1]]
                matches = matches[matches[:, 2].argsort()[::-1]]
                matches = matches[np.unique(matches[:, 0], return_index=True)[1]]
                .......

Why is there such a difference? It seems more reasonable to calculate the confusion matrix in this way, considering the order of calculating the maximum IOU twice. When using the first calculation method, for a GT with multiple pre target boxes that can be matched, it will default to matching the first target that meets the requirements, rather than the target box corresponding to the maximum iou.

[glenn-jocher]

@zwcity hello!

The difference in approaches for matching predictions to true labels between mAP calculation and confusion matrix generation primarily stems from their distinct objectives and the metrics they aim to optimize.

For mAP calculation, the goal is to evaluate the model's precision and recall across different confidence thresholds, which often involves sorting by IoU and then by confidence to prioritize higher quality predictions. This method ensures that the best possible matches are considered first, enhancing the precision metric crucial for mAP.

On the other hand, the confusion matrix computation focuses more on the overall accuracy and class-wise performance, where sorting by confidence twice helps in accurately assigning predictions to true labels, reflecting a more direct measure of prediction correctness.

Each method is tailored to provide the most relevant insights for its respective metric, which is why you see a variation in the sorting and matching process. This nuanced approach helps in fine-tuning the performance evaluation according to the specific needs of each metric.

If you have more questions or need further clarification, feel free to ask. Happy to help! 😊

[github-actions]

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