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Quality of background detection when single_cls = True #13091
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👋 Hello @satyrmipt, 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. If this is a 🐛 Bug Report, please provide a minimum reproducible example to help us debug it. If this is a custom training ❓ Question, please provide as much information as possible, including dataset image examples and training logs, and verify you are following our Tips for Best Training Results. RequirementsPython>=3.8.0 with all requirements.txt installed including PyTorch>=1.8. To get started: git clone https://github.com/ultralytics/yolov5 # clone
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@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
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}")
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! |
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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.
Additional
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