Roc curve /part 2 about yolov5

[glenn-jocher]

Hello! 🚀 It looks like you're experiencing issues with generating the ROC curve because you're not using ground truth labels.

To properly calculate AUC for ROC, you will need the actual labels (ground truth) for each prediction to compare against. It seems you only have the detection confidence scores.

Here’s a method to modify your script to include ground truth labels:

1. Update the y_true list in your loop to include actual labels from your dataset.
2. Map your class labels to 0 or 1 based on the detection (presence or absence of your target class).

Modify this part of your script:

# Assume your actual labels column in CSV is the second column (index 1)
labels = data[1].tolist()  # adjust index based on your data structure
...
y_true.extend(labels)

Ensure you're reading from the correct column index for labels and scores in your CSV. After this adjustment, the ROC calculation should function with accurate class labels.

Hope this helps you move forward! 👍