I am new to computer vision and I have a simple question that could not get any answer for on the web. I am using mask rcnn implementation by Matterport to perform a binary classification on some images and I have some extra lines of code that compute the mAP for each image. Now I would like to know, if I can add up the mAPs calculated for each image and then divide the number to get mAP for the whole dataset, and if not, how can I compute the overall mAP? (preferrably using the utilities of the mask rcnn model)
How to calculate mAP (mean average precision) for the whole dataset?
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Yes, you can do something like
where
num_testis number of test imagesJust keep training and test data separate.