Closed Waariss closed 6 months ago
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@Waariss hello! Thanks for reaching out with your questions. Let's address them one by one:
Handling of Background Class: YOLOv5 does not explicitly infer a 'background' class during training or detection. If you're seeing a 'background' class in your confusion matrix, it might be due to an issue with the labeling or the interpretation of the matrix. Ensure that your dataset labels are consistent and that there are no unintended classes. The confusion matrix should only display the classes that are present in your dataset.
Evaluation Metrics for Image Detection: For a single-class detection task, mAP (mean Average Precision) is indeed a suitable metric. It provides a good balance between precision and recall across different detection thresholds. However, if you're interested in the model's performance on specific aspects, such as its ability to reduce false positives or false negatives, you might also consider looking at precision, recall, and F1-score for your disease class. These metrics can give you a more detailed understanding of where your model excels or needs improvement.
Remember, the key to a successful model is not just in the metrics but also in ensuring that your dataset is well-prepared and that the model is properly tuned for your specific task. If you need further guidance on interpreting evaluation metrics or improving your model, our documentation at https://docs.ultralytics.com/yolov5/ provides comprehensive information.
Keep up the great work on your project, and if you have any more questions or need clarification, feel free to ask. The YOLO community and the Ultralytics team are here to help! 😊🐔
Thank you for your help @glenn-jocher . So can I just ignore the 'background' class in the Confusion Matrix during training or detection? Because I want to focus only on image detection (ROI) and further use it in CNN for image classification.
@Waariss, you're welcome! If the 'background' class is showing up in your confusion matrix and you're certain it's not part of your dataset, it's likely an artifact of how the matrix is being generated or visualized. You should:
For your project, you can focus on the metrics for your disease class and disregard the 'background' if it's not relevant. When using the detections as regions of interest (ROIs) for further CNN classification, ensure that your detection model is accurately distinguishing the disease from the rest of the image.
If you continue to see unexpected results or have any concerns, feel free to reach out for support. Good luck with your chicken disease detection and classification project! 🐤🔍
- Handling of Background Class: YOLOv5 does not explicitly infer a 'background' class during training or detection. If you're seeing a 'background' class in your confusion matrix, it might be due to an issue with the labeling or the interpretation of the matrix. Ensure that your dataset labels are consistent and that there are no unintended classes. The confusion matrix should only display the classes that are present in your dataset.
@glenn-jocher maybe there is still a gap in my understanding/missing something on my end, but I still get "background" in the confusion matrix even if my labels and detections do no have "background" as a class. Here (screenshot below) I print the min, max of some meta info that I collect during validation. As you see, the min/max of gt and pred is 0/3. However in the confusion matrix below still comes with "background". And it looks like a whole lot of detections are predicted by the model as "background"
@ashwin-999, thank you for providing additional details and screenshots. It sounds like you've done a thorough check on your end regarding the dataset and labels. Given the information you've shared, it seems there might be a misunderstanding regarding how the confusion matrix is generated and interpreted in the context of YOLOv5.
In YOLOv5, and object detection models in general, detections that do not sufficiently overlap with any ground truth object (as determined by a threshold on metrics like Intersection over Union, IoU) are typically considered false positives. In a single-class detection scenario without an explicit 'background' class, these detections might be visually or programmatically represented in a way that suggests they are 'background' detections, especially if the tool or script generating the confusion matrix includes a row or column for these cases.
The presence of a significant number of detections classified as 'background' could indicate a high rate of false positives, where the model is detecting objects or areas that it strongly believes to be of interest but are not labeled as such in your dataset. This could be due to various factors, including:
To address this issue, consider the following steps:
Remember, the goal is to balance sensitivity (true positive rate) and specificity (true negative rate) according to the needs of your specific application.
If after these steps you're still encountering issues or if there's anything more we can do to assist, please let us know. Your project's success is important to us, and we're here to help in any way we can. Keep up the great work! 🌟
@glenn-jocher , in my case i got the unusual for probabilities where the sum up for some of my labels were more than 1 in a confusion matrix.
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| | | | | | | | | | Summation -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- PREDICTED | Datum_Triangle-Datum_Box | 0.98 | 0.01 | | | | | | 0.04 | 1.03 Dimension_Tolerance | | 0.62 | | | 0.17 | | 0.06 | 0.25 | 1.1 Perpendicularity_Tolerance | | | 0.86 | | | | | 0.06 | 0.92 Position_Tolerance | | 0.02 | 0.07 | 0.92 | | | | 0.12 | 1.13 Radial_Tolerance | | 0.03 | | 0.03 | 0.33 | | | 0.03 | 0.42 SurfaceProfile_Tolerance | | | | | | 1 | | 0.01 | 1.01 Theoretical_Exact_Dimension | | 0.04 | | 0.03 | | | 0.75 | 0.48 | 1.3 background | 0.02 | 0.28 | 0.07 | 0.03 | 0.5 | | 0.2 | | 1.1 | | Datum_Triangle-Datum_Box | Dimension_Tolerance | Perpendicularity_Tolerance | Position_Tolerance | Radial_Tolerance | SurfaceProfile_Tolerance | Theoretical_Exact_Dimension | background | | | TRUE | | Summation | 1 | 1 | 1 | 1.01 | 1 | 1 | 1.01 | 0.99 |
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Question
I am using YOLOv5 for a project aimed at detecting a specific chicken disease from fecal images. The model has been trained on a dataset designed for this single-class detection task. However, upon evaluating the trained model, I've observed the presence of a 'background' class in the confusion matrix, which has led to some uncertainty on how to proceed.
Dataset and Training Configuration: The dataset comprises images labeled for the presence of a particular chicken disease, with no explicit annotations for a background class. After training, the confusion matrix shows both the target disease class and an unexpected 'background' class.
Concerns with Background Class: The appearance of this 'background' class in the confusion matrix is puzzling, as my primary goal is to detect the disease presence accurately. This has raised questions about the implications of the background class on the model's detection performance and how it should be addressed in the training or evaluation process.
Questions
Handling of Background Class: Is the 'background' class automatically inferred by YOLOv5, and should it be a concern in the context of a single-class detection project? How does the inclusion of this class affect the model's performance, and is there a recommended approach to managing or ignoring it during training and evaluation?
Evaluation Metrics for Image Detection: Given that my project is focused exclusively on the detection of a single class, is mAP the most appropriate metric to gauge the model's effectiveness? Are there specific considerations or additional metrics I should employ to more accurately assess detection performance, especially in light of the background class issue?