Quality of background detection when single_cls = True

[satyrmipt]

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Question

I'm trying to teach the model to detect objects of particular classes without classification. All i need is bounding boxes, not class labels. Backgrounds are included in dataset as recommended (no labels just images).

In this case confusion matrix is 2x2 matrix where intersection of "background true" and "background predicted" always is equal to zero by design. How can i measure how good is my model on the task "separate background images and images with any class"? Sure, i can apply finetuned model to images one by one and calculate the this metrics manually but may be there are specific metric in yolo results?

I need this metric to decide if i need more backgrounds to be added in the dataset.

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[glenn-jocher]

@satyrmipt hello,

Thank you for reaching out and for providing detailed information about your use case. To address your question regarding the quality of background detection when single_cls=True, here are a few points to consider:

1. Reproducible Example: To better understand and potentially reproduce your issue, could you please provide a minimum reproducible code example? This will help us investigate the problem more effectively. You can refer to our guidelines on creating a minimum reproducible example here.

2. Version Check: Ensure that you are using the latest versions of torch and the YOLOv5 repository. You can update your packages with the following commands:

   pip install --upgrade torch
   git pull https://github.com/ultralytics/yolov5

3. Background Detection Metrics: YOLOv5 does not provide a specific metric for evaluating the separation of background images from images with objects directly. However, you can use the following approach to assess your model's performance:

   • Precision and Recall: Calculate precision and recall for the background class. Precision will tell you how many of the predicted backgrounds are actually backgrounds, and recall will tell you how many of the actual backgrounds were correctly identified.
   • Custom Metrics: Implement a custom metric that evaluates the model's ability to distinguish between background and object-containing images. This can be done by iterating over your dataset and calculating the true positives, false positives, true negatives, and false negatives for the background class.

Here's a simple example of how you might calculate these metrics manually:

from sklearn.metrics import confusion_matrix

# Assuming y_true and y_pred are your true and predicted labels
# where 0 represents background and 1 represents any object
y_true = [0, 1, 0, 1, 0, 0, 1, 1]
y_pred = [0, 1, 0, 0, 0, 1, 1, 1]

# Calculate confusion matrix
cm = confusion_matrix(y_true, y_pred)

# Extract true negatives, false positives, false negatives, and true positives
tn, fp, fn, tp = cm.ravel()

# Calculate precision and recall for background class
precision_bg = tn / (tn + fn)
recall_bg = tn / (tn + fp)

print(f"Precision (Background): {precision_bg}")
print(f"Recall (Background): {recall_bg}")

4. Dataset Balance: Ensure that your dataset is balanced with a sufficient number of background images. This can help improve the model's ability to distinguish between background and object-containing images.

Feel free to share any additional details or code snippets that could help us assist you better. Thank you for your cooperation and for being a part of the YOLO community!