I am wondering is it possible to have two true positive predictions for one bounding box ground truth only. Following this section from Stanford . They define truth positive like this: We start with IoU to decide if each prediction is correct or not. For a ground truth object and nearby prediction, if the predicted class matches the actual class, and the IoU is greater than a threshold, we say that the network got that prediction right (true positive). Otherwise, the prediction is a false positive. Now, take an example, in the image below, the red box is ground truth and the black box is prediction. Assume both black boxes predict correctly in the classification task, and both yellow areas are bigger than IoU threshold. In this case, both of them satisfy the condition to be true positive, then the true positive is not "true" anymore?

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