ConfusionMatrix incorrect?

[GilSeamas]

Search before asking

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

YOLOv5 Component

Validation

Bug

I believe there is a problem in the ConfusionMatrix() method. It only counts the false positives if there are true positives.

n = matches.shape[0] > 0
m0, m1, _ = matches.transpose().astype(int)
for i, gc in enumerate(gt_classes):
j = m0 == i
if n and sum(j) == 1:
self.matrix[detection_classes[m1[j]], gc] += 1 # correct
else:
self.matrix[self.nc, gc] += 1 # true background

if n:
for i, dc in enumerate(detection_classes):
if not any(m1 == i):
self.matrix[dc, self.nc] += 1 # predicted background

I believe it should be:

n = matches.shape[0] > 0
m0, m1, _ = matches.transpose().astype(int)
for i, gc in enumerate(gt_classes):
j = m0 == i
if n and sum(j) == 1:
self.matrix[detection_classes[m1[j]], gc] += 1 # correct
else:
self.matrix[self.nc, gc] += 1 # true background

#NO IF STATEMENT HERE
for i, dc in enumerate(detection_classes):
if not any(m1 == i):
self.matrix[dc, self.nc] += 1 # predicted background

Environment

No response

Minimal Reproducible Example

n = matches.shape[0] > 0
m0, m1, _ = matches.transpose().astype(int)
for i, gc in enumerate(gt_classes):
j = m0 == i
if n and sum(j) == 1:
self.matrix[detection_classes[m1[j]], gc] += 1 # correct
else:
self.matrix[self.nc, gc] += 1 # true background

if n:
for i, dc in enumerate(detection_classes):
if not any(m1 == i):
self.matrix[dc, self.nc] += 1 # predicted background

Additional

No response

Are you willing to submit a PR?

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

[github-actions]

👋 Hello @GilSeamas, 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]

@GilSeamas hello! Thank you for bringing this to our attention and for your willingness to help by submitting a PR. It's great to see community members actively participating in improving YOLOv5.

From your description, it seems you've identified a potential issue with the way false positives are counted in the ConfusionMatrix() method. Your observation about the conditional structure that might prevent counting false positives when there are no true positives is insightful.

Before proceeding with a PR, it would be beneficial to verify this behavior with a controlled test case to ensure that the proposed change resolves the issue without introducing any new ones. If you could create a minimal reproducible example that demonstrates the problem and confirms that your suggested fix works, that would be fantastic.

Once you have this, please go ahead and submit a PR with the changes and the test case. We'll review it as soon as possible. Your contribution is much appreciated! 🚀

If you need guidance on creating a PR or on how to write the test case, please refer to our documentation at https://docs.ultralytics.com/yolov5/.

Thanks again for your support and for being an active member of the YOLOv5 community! 😊

[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.

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