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https://shubhamchauhan125.medium.com/map-mean-average-precision-for-object-detection-with-simple-python-demonstration-dcc7b3850a07
Understanding mean Average Precision for Object Detection (with Python Code)
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5 Comments
Can you please explain the formula of FN in the above program ..
ReplyDeleteThanks,
Karuna Sree
Thanks for the blog ... I couldn't understand the formula FN = len(eval_table['TP/FP']== 'TP') .... can you please help me ..
ReplyDeleteThanks,
Karuna Sree
It is mentioned above that if IOU>0.5 then it will be true positive and also if IOU>0.5 but object is miss classified then it will be flase negative(FN). So FN is the number of count for IOU>0.5 which is TP.
DeleteThank you .. But how are you getting the misclassified ... Not all true positives are not FN right ? Are you comparing that classification difference here ?
DeleteYes, you are right that all TP are not FN but finding FN will complicate the code. So, just for simplicity we are considering them equal to TP. You can make it zero or you can replace some TP with FN in the data for your calculation and understanding. For better understanding I will mention this assumption in the article.
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