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?