<p>I have a general question regarding the <code>mAP</code> score used in measuring object detection system performance. </p>

<p>I understood how the <code>AP</code> score is calculated, by averaging precision over recall 0 to 1. And then we can compute <code>mAP</code>, by averaging <code>AP</code> score of different labels.</p>

<p>However, what I have been really confused, is that, it seems that <code>mAP</code> score is used to denote the "precision" of a model. Then what about the "recall" aspect? Note that generally speaking, when measuring the performance of a machine learning model, we need to report <code>precision</code> and <code>recall</code> at the same time, right? It seems that <code>mAP</code> can only cover the <code>precision</code> aspect of a model.</p>

<p>Am I missed anything here? Or <code>mAP</code> score, despite its name is derived from <code>Precision</code>, can indeed subsume both "precision" and "recall" and therefore become comprehensive enough? </p>

CC BY-SA 4.0
2019-08-21T20:27:19.287Z
average precision is relative to recall, its says something about the precision recall give/take.
mshlis
CC BY-SA 4.0
2019-08-22T01:15:00.670Z
@mshlis. Thanks for the clarification. Yes, that's what I can think about.. But is there any more "formal" description/statement such that "mAP is derived from both precision or recall"?
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CC BY-SA 4.0
2019-08-22T01:15:44.803Z
Look at F score
mshlis
CC BY-SA 4.0
2019-08-22T01:24:19.243Z
@mshlis Yes, I understood the definition of F score, but what's the relation with mAP?
lllllllllllll
CC BY-SA 4.0
2019-08-22T01:26:47.457Z
@mshlis OK, I think I found what I am looking for. What I can say is that "mAP derieves from both precision and recall values, by computing the area under the precision-recall curve for a given class." In that sense, yes, it surely subsumes both precision and recall values in a way.
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<object-detection>
Can mAP score be used to describe "recall" rate of a model?
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# Can mAP score be used to describe "recall" rate of a model?

(Asked by: username_0 on 2019-08-21)

I have a general question regarding the `mAP` score used in measuring object detection system performance.

I understood how the `AP` score is calculated, by averaging precision over recall 0 to 1. And then we can compute `mAP`, by averaging `AP` score of different labels.

However, what I have been really confused, is that, it seems that `mAP` score is used to denote the "precision" of a model. Then what about the "recall" aspect? Note that generally speaking, when measuring the performance of a machine learning model, we need to report `precision` and `recall` at the same time, right? It seems that `mAP` can only cover the `precision` aspect of a model.

Am I missed anything here? Or `mAP` score, despite its name is derived from `Precision`, can indeed subsume both "precision" and "recall" and therefore become comprehensive enough?

**Tags:** object-detection

> **Comments:**
> **username_1**: average precision is relative to recall, its says something about the precision recall give/take.  
> **username_0**: @username_1. Thanks for the clarification. Yes, that's what I can think about.. But is there any more "formal" description/statement such that "mAP is derived from both precision or recall"?  
> **username_1**: Look at F score  
> **username_0**: @username_1 Yes, I understood the definition of F score, but what's the relation with mAP?  
> **username_0**: @username_1 OK, I think I found what I am looking for. What I can say is that "mAP derieves from both precision and recall values, by computing the area under the precision-recall curve for a given class." In that sense, yes, it surely subsumes both precision and recall values in a way.  

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## 0 answers

