I wonder how can I add ROC curves, add Sensitivity, Specificity, Accuracy, and AUC evaluation metrics in YOLOv8's multiclassification detection?

[yuanyuanpei7]

I wonder how can I add ROC curves, add Sensitivity, Specificity, Accuracy, and AUC evaluation metrics in YOLOv8's multiclassification detection?

[pderrenger]

@yuanyuanpei7 to add ROC curves and calculate Sensitivity, Specificity, Accuracy, and AUC metrics in YOLOv8's multiclass object detection, you would need to modify the validation code to compute these metrics. YOLOv8's default validation metrics include precision, recall, and mAP, but you can extend this functionality by following these steps:

1. Collect Scores and Labels: During validation, collect the confidence scores of your predictions and the ground truth labels for each class.

2. Calculate Metrics: Use the collected scores and labels to calculate True Positive (TP), False Positive (FP), True Negative (TN), and False Negative (FN) counts for each threshold level. Then, compute Sensitivity (TPR), Specificity (TNR), and Accuracy for each threshold.

3. ROC Curve: Plot the ROC curve by plotting TPR against FPR at various threshold settings.

4. AUC: Calculate the Area Under the ROC Curve (AUC) to quantify the overall performance of the model.

You can use libraries like scikit-learn to help with these calculations. However, this functionality is not built into YOLOv8 by default, so you would need to implement this as a custom feature. Please refer to the Val Examples for guidance on how to work with validation results, and you can then extend this with your custom metric calculations.

Remember to test your custom code thoroughly to ensure that the metrics are being calculated correctly. 🧪📊

[Jacko760]

im curious that can't we get the values of TP FP TN directly from confusion matrix that's already part of yolo function and im able to collect the scores by setting save-json to true

[pderrenger]

Hi @Jacko760,

Great question! Yes, you can indeed derive the values of True Positives (TP), False Positives (FP), True Negatives (TN), and False Negatives (FN) directly from the confusion matrix generated by YOLOv8. Here's a brief overview of how you can do this:

1. Extract Confusion Matrix: When you set save-json to true, YOLOv8 saves the confusion matrix along with other metrics. You can use this matrix to calculate TP, FP, TN, and FN.

2. Calculate Metrics:

   • TP: The diagonal elements of the confusion matrix represent the True Positives for each class.
   • FP: The sum of the respective column minus the diagonal element gives the False Positives.
   • FN: The sum of the respective row minus the diagonal element gives the False Negatives.
   • TN: The sum of all elements in the matrix minus the sum of the row and column for the class plus the diagonal element.

Here's a small code snippet to illustrate this:

import numpy as np

# Example confusion matrix
conf_matrix = np.array([
    [50, 2, 1],
    [3, 45, 2],
    [1, 2, 47]
])

# Calculate TP, FP, FN, TN for each class
TP = np.diag(conf_matrix)
FP = np.sum(conf_matrix, axis=0) - TP
FN = np.sum(conf_matrix, axis=1) - TP
TN = np.sum(conf_matrix) - (FP + FN + TP)

print(f"TP: {TP}")
print(f"FP: {FP}")
print(f"FN: {FN}")
print(f"TN: {TN}")

3. Compute Additional Metrics:
   • Sensitivity (Recall): ( \text{Sensitivity} = \frac{TP}{TP + FN} )
   • Specificity: ( \text{Specificity} = \frac{TN}{TN + FP} )
   • Accuracy: ( \text{Accuracy} = \frac{TP + TN}{TP + TN + FP + FN} )
   • AUC: Use libraries like scikit-learn to compute the ROC curve and AUC.

If you haven't already, please ensure you're using the latest version of YOLOv8 to benefit from the latest features and fixes. For more detailed guidance on creating a reproducible example, you can refer to our Minimum Reproducible Example documentation.

Feel free to reach out if you have any more questions! 😊

[Jacko760]

Thanks for the detailed explanation; I understand it now.

I want to ensure that I can extract True Negatives (TN), False Negatives (FN), etc. Plotting the ROC curve involves plotting the True Positive Rate (TPR) against the False Positive Rate (FPR). Therefore, I don’t need to compare it with the ground truth table since the confusion matrix already provides the combined results.

However, when I use libraries like Scikit-learn to plot the ROC curve, they expect two arguments. How can I handle this scenario?

correct my lingo if I’m off the track

[pderrenger]

Hi @Jacko760,

I'm glad the explanation helped! You're on the right track with your understanding. To plot the ROC curve using libraries like Scikit-learn, you typically need the true binary labels and the predicted scores (probabilities) for each class.

Here's a step-by-step approach to handle this scenario:

1. Extract Scores and Labels: Ensure you have the true labels and predicted scores for each instance. YOLOv8's save-json option can help you collect these during validation.

2. Prepare Data for Scikit-learn:

   • True Labels: A binary array indicating the presence of each class.
   • Predicted Scores: The confidence scores for each class.

3. Plot ROC Curve:

   • Use Scikit-learn's roc_curve and roc_auc_score functions to compute and plot the ROC curve.

Here's a code example to illustrate this:

from sklearn.metrics import roc_curve, roc_auc_score
import matplotlib.pyplot as plt

# Example true labels and predicted scores
true_labels = [0, 1, 1, 0, 1, 0, 1, 0, 0, 1]
predicted_scores = [0.1, 0.4, 0.35, 0.8, 0.65, 0.2, 0.9, 0.3, 0.15, 0.85]

# Compute ROC curve and ROC area
fpr, tpr, _ = roc_curve(true_labels, predicted_scores)
roc_auc = roc_auc_score(true_labels, predicted_scores)

# Plot ROC curve
plt.figure()
plt.plot(fpr, tpr, color='darkorange', lw=2, label=f'ROC curve (area = {roc_auc:.2f})')
plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Receiver Operating Characteristic (ROC) Curve')
plt.legend(loc="lower right")
plt.show()

This example assumes you have binary labels and predicted scores for a single class. For multiclass scenarios, you would typically compute the ROC curve for each class separately.

If you encounter any issues or need further assistance, please ensure you're using the latest version of YOLOv8 and provide a reproducible example if possible. This helps us diagnose and resolve issues more effectively. You can refer to our Minimum Reproducible Example guide for more details.

Feel free to reach out with any more questions! 😊