Yolov5 confusion matrix with background FP=1 and TN=0 #11194
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I have the same issue of interpreting this kind of results, however my interpretation would be that the columns do not depend on each other as you could assume in 2x2 simple confusion matrix. The 2x2 confusion matrix that I have in mind should ideally look like [1, 0; 0 1]. Because the columns are normalized, the sum of the columns has to be 1, but the sum of the rows can be over 1. In your case I would say that if the actual object was truck, the model predicted truck in 74% and in 26% predicted background. I am still a little confused about the background, because @glenn-jocher here: #1665 (comment) said, that the background is not predicted, so this could be the reason for the results in background column? |
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@jbezovsek thank you for sharing your concerns about interpreting the confusion matrix for YOLOv5. It can be confusing to understand the results of the matrix, especially when dealing with single-class detection and background. You are correct that the columns of this matrix do not necessarily depend on each other, as in a simple 2x2 confusion matrix. In the case of YOLOv5, the columns represent the predicted classes, and they are normalized. As a result, the sum of each of the columns would be equal to 1. Regarding the background class, according to the YOLOv5 developers, it is not predicted by the model. Therefore, it is possible that the TN value being 0 is a result of not having any true negative samples for the background class in the validation set. Once again, thank you for your question, and please let us know if you have any further concerns about YOLOv5 or vision AI in general. |
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Hello @glenn-jocher, is that mean for single class that has FP=1 and TN=0 on background class is because we don't have any samples of background on validation set. Not because our model is wrong, right? Because i have the same issue above, my FP=1 and TN=0 for single class label |
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Hello @ilhamalvindo, thank you for reaching out with your question regarding YOLOv5's confusion matrix and understanding the results. You are correct. In a single-class detection with background, if the TN value is 0, it likely means that there were no true negative samples for the background class in the validation set. Therefore, it does not necessarily imply that your model is wrong. Similarly, if the FP value is 1, it could mean that there was only one false positive detected as a background. Please let me know if you have any further questions or concerns. |
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Thank you for the explanation! @glenn-jocher |
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@ilhamalvindo you're welcome, happy to be of help! If you have any further questions or issues with YOLOv5, please don't hesitate to ask. |
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👋 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:
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 ⭐ |
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@RyanTNN hi there! It seems like your link to the confusion matrix image is not accessible. However, I will still address your questions based on the information provided.
Feel free to provide additional details or share the confusion matrix image for a more precise analysis or assistance. |
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Why does the confusion matrix give me 100% in the background in yolov7 and v8 |
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@kevinkwabena hello! It seems there might be a misunderstanding. I'm the author and maintainer of the Ultralytics YOLOv5 repository, and YOLOv7 and v8 are not part of the Ultralytics projects. They are developed by different teams and may have different implementations and behaviors. Regarding your question about a confusion matrix showing 100% in the background, this typically indicates that the model is predicting the background class for all the samples, which could be due to several reasons such as model overfitting, incorrect labeling, or issues with the validation dataset. For specific help with YOLOv7 or v8, I would recommend reaching out to the maintainers of those repositories or checking their documentation and issues for similar cases and solutions. If you have questions about YOLOv5, I'd be more than happy to assist you! |
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@glenn-jocher Hi! It seems the issue still persists for YOLOv5, I'm not observing any True Negatives (TN) or False Positives (FP) in the confusion matrix. This occurs despite my validation set including both negative and positive images, though labels are only provided for the positive ones. My YAML file specifies only one class, "smoke". The problem remains the same whether I'm running train.py or val.py. |
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@Leonardbd hello! Thanks for reaching out with your confusion matrix concern. 😊 Based on what you've described, it appears your model isn’t recognizing any True Negatives (TN) or False Positives (FP) because your validation set only includes labels for positive cases ("smoke"). For TN and FP to appear, there need to be instances where your model predicts "no smoke" correctly (TN) or incorrectly (FP), which requires labeled negative (no smoke) images in your dataset. If you haven't already, ensure your dataset includes explicitly labeled negative images (images without smoke) and that they're correctly referenced in your dataset YAML file. This adjustment should help the model recognize and learn from both the presence and absence of smoke, potentially resolving the issue you're observing with the confusion matrix. Quick tip: Make sure your 'train' and 'val' keys in the YAML file accurately reflect your dataset's structure, including paths to both positive and negative samples. Let me know if this helps, or if you have further questions! |
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@glenn-jocher thank you for your guidance! Based on your advice, it seems I need to include a separate class in the YAML file for images without smoke, labeled as "nosmoke". I've updated my YAML file accordingly: names: However, I'm unsure about how to proceed with annotating the negative images (those without smoke). Since these images inherently lack the object of interest and thus don't have bounding boxes, how should their corresponding .txt annotation files be formatted? Should they simply include a "1" to denote the "nosmoke" class, and if so, how do we address the absence of bounding box coordinates? I appreciate your assistance in clarifying this matter. |
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@Leonardbd glad to hear you're making progress! 😊 For annotating negative images (no object of interest like "nosmoke"), you should create an empty .txt annotation file for each image. There's no need to include a "1" or any other class identifier in these files. Simply having the empty .txt file corresponding to the image tells YOLOv5 that there are no objects present in that image. Here's a quick example:
This effectively informs the model that "image123.jpg" contains no objects to be detected, helping it learn the "nosmoke" instances without needing explicit bounding boxes. Keep up the good work, and feel free to reach out if you have more questions! |
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Hi there! 👋 It sounds like you're experiencing an issue where the model predicts every image as containing a PCB. If your dataset includes negative samples ("null" in your case) but the confusion matrix still shows zero background detection, it might be due to how these "null" images are annotated or handled during training. Ensure that:
If both these are in place and you're still seeing issues, it might be worthwhile to double-check the balance and variety in your dataset or consider further tuning your model's hyperparameters. Let me know if this helps or if there's anything else you'd like to explore! 😊 |
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Hello Glenn, I hope you're doing well thanks for the fast reply on my previous email and now I got a new question for you
So, I got this confusion matrix for predicting and classifying PCBs from other objects and i was wondering if I could delete the background row and column from it.
Sincerely,
Rashed
…________________________________
From: Glenn Jocher ***@***.***>
Sent: Tuesday, May 7, 2024 8:21 AM
To: ultralytics/yolov5 ***@***.***>
Cc: RashedAshqar ***@***.***>; Comment ***@***.***>
Subject: Re: [ultralytics/yolov5] Yolov5 confusion matrix with background FP=1 and TN=0 (Issue #11194)
Hi there! 👋
It sounds like you're experiencing an issue where the model predicts every image as containing a PCB. If your dataset includes negative samples ("null" in your case) but the confusion matrix still shows zero background detection, it might be due to how these "null" images are annotated or handled during training.
Ensure that:
* Your "null" images have corresponding empty annotation files in your dataset. An empty .txt file for these images indicates to the model that there are no objects present.
* Your training dataset configuration in your YAML does correctly reference these negative samples alongside the positive ones.
If both these are in place and you're still seeing issues, it might be worthwhile to double-check the balance and variety in your dataset or consider further tuning your model's hyperparameters.
Let me know if this helps or if there's anything else you'd like to explore! 😊
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Hi Rashed, Good to hear from you! Regarding your question about removing the background row and column from the confusion matrix, it's technically feasible to exclude these in your visualization if they aren't relevant to your analysis. However, doing so might limit the insight you can gain regarding how well your model is distinguishing between PCBs and true negatives (background). Generally, it's useful to see all aspects of your model's performance, including background predictions, to assess any potential biases or issues. If you still wish to modify the matrix, this typically involves adjusting the code that generates or visualizes the confusion matrix in your analysis scripts. You would remove the data associated with the background before plotting. Let me know if you need further assistance or specific code help! Cheers 😊 |
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Hi @glenn-jocher, I want to clarify this, for all the anchors we get the object_Score which tells whether the object present in that region or not. if the that object_Score is 0 then it is consider as background - is it right? In this answer, you have mentioned that there is no background class samples annotated on validation dataset[TN], that's why the FP showing 100% for the object class - that means for eg, Is the above statements are correct, Could you clarify this, thanks |
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Hi @vishakraj64, Thank you for your detailed question! Let's clarify how the object score and background detection work in YOLOv5. Object Score and Background DetectionIn YOLOv5, each anchor box predicts an objectness score, which indicates the likelihood of an object being present in that region. If the objectness score is low (close to 0), it suggests that the region is likely background. However, YOLOv5 does not explicitly classify regions as "background"; instead, it focuses on detecting objects of interest. Single-Class DetectionFor single-class detection, if your model incorrectly predicts objects in regions that are actually background, these will be counted as False Positives (FP). For example:
Multi-Class DetectionFor multi-class detection, the FP calculation is similar but distributed across multiple classes. For example:
Clarification on Your StatementsYour understanding is mostly correct, but let's refine it:
Example CalculationFor multi-class detection:
Next StepsIf you encounter any issues or bugs, please ensure you are using the latest versions of Feel free to reach out with any more questions or clarifications! |
Hello,
I was training a model to detect trucks in pictures and here is the result confusion matrix of my val data. Following the convention of reading a confusion matrix, I'll call TP for the upper left cell, FP for upper right cell, FN for lower left cell and TN for lower right cell. (please ignore the class 'bicycle' and 'person' because the original dataset only have trucks labeled but 'truck' was assigned a 'class=2' during the manual annotation)
For the 'background', there are 1 and 0. I searched online and found a lot of people are having the same issue, here are some examples:
yolov5 issue 10365
yolov5 issue 1665
stackoverflow
In yolov5 issue 1665, I noticed @glenn-jocher gave a brief explanation that 'columns are normalized', but I'm still quite confused. My I get a more clear explanation about why it happens and is there a possible way to fix it?
Another thing bothering me is that actually I didn't have any annotation of background in my training data, so I guess that's why TN=0?
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