I use a yolo (y) to detect only one object and a multiclassifier (mc) that classifies that object. Now, the problem is: what I have to do with yolo's false positive and false negative, if I want to compute the whole system accuracy, precision and recall?

Now I'm computing overall accuracy like this:

acc = (tp_mc + tn_mc) / (tp_mc + tn_mc + fp_mc + fn_mc + fn_y + fp_y)

To compute precision and recall I'm doing that for each class of mc:

precision_i = tp_mc_i / (tp_mc_i + fp_mc_i + fp_y_i)

recall_i = tp_mc_i / (tp_mc_i + fn_mc_i + fn_y_i)

Where fp_y_i and fn_y_i are the yolo's false positive and false negative that belongs to the class i of the multiclassifier. Do you think that this is the correct way to compute accuracy, precision and recall?


1 Answer 1


What matters is whether the instance is classified correctly or not at the end, so it's only about mapping correctly the cases from the y classifier:

  • tp_y is easy, it's the classification status of the mc classifier
  • tn_y is a TN for every class $i$.
  • fp_y is a FP for the class $i$ predicted by mc and a TN for every other class.
  • fn_y is a FN for the true class $i$ and a TN for every other class.

I'm not sure what you count as tn_mc here? Normally in a multiclass setting accuracy is the sum of $TP_i$ for every class $i$, because there's no TN. That would give us:


With $n$ the total number of instances (from the start, not only the ones fed to mc)

Your precision and recall formulas look correct to me, assuming that fp_y_i are the FP instances where $i$ is the predicted class whereas fn_y_i are the FN instances where $i$ is the true class.


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