Confusion Matrix Missing False Negatives

[jbutle55]

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• I have searched the YOLOv5 issues and found no similar bug report.

YOLOv5 Component

Validation

Bug

In val.py, during the "Evaluate" stage, a single batch worth of metrics for the confusion matrix is computed using:

                if plots:
                    confusion_matrix.process_batch(predn, labelsn)

However, if the length of predictions for the image in question is zero, this portion of code is skipped over due to:

            if len(pred) == 0:
                if nl:
                    stats.append((torch.zeros(0, niou, dtype=torch.bool), torch.Tensor(), torch.Tensor(), tcls))
                continue

If this continue statement is called then this batch is not processed for the confusion matrix, but if the relevant image had ground truth objects, meaning these were missed detections since len(pred) was 0, then these FNs won't be accounted for in the confusion matrix.

Environment

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Minimal Reproducible Example

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Additional

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Are you willing to submit a PR?

• Yes I'd like to help by submitting a PR!

[github-actions]

👋 Hello @jbutle55, 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.

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[glenn-jocher]

@jbutle55 hi, thank you for your feature suggestion on how to improve YOLOv5 🚀!

The fastest and easiest way to incorporate your ideas into the official codebase is to submit a Pull Request (PR) implementing your idea, and if applicable providing before and after profiling/inference/training results to help us understand the improvement your feature provides. This allows us to directly see the changes in the code and to understand how they affect workflows and performance.

Please see our ✅ Contributing Guide to get started.

[jbutle55]

This got merged, closing the issue.

[glenn-jocher]

Thank you for your contribution, @jbutle55! Your assistance is greatly appreciated. 🎉 We're grateful for your help in enhancing YOLOv5, and we look forward to any future contributions you may make. If you have any more ideas or feedback, feel free to share them!