Answers.Body: <p>Nice question got me thinking, hope these help you understand better <a href="https://alexeyab84.medium.com/hi-jonathan-hui-d2f2baa615ad" rel="nofollow noreferrer">Source</a></p>
  <ol>
  <li>MS COCO — uses 101-Recall-points on PR-chart <a href="https://jonathan-hui.medium.com/map-mean-average-precision-for-object-detection-45c121a31173" rel="nofollow noreferrer">mAP (mean Average Precision) for Object Detection</a></li>
  <li>PascalVOC2007 — uses 11-Recall-points on PR-chart</li>
  <li>PascalVOC2010–2012 — uses Area-Under-Curve on PR-chart</li>
  <li>ImageNet — uses Area-Under-Curve on PR-chart</li>
  </ol>
  <p>I too have to understand it further</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2022-05-05T18:07:19.587Z
Answers.LastActivityDate: 2022-05-05T18:07:19.587Z
Answers.UserDisplayName: Santhosh Dhaipule Chandrakanth
Body: <p>I know that mAP (mean Average Precision) is the common evaluation metric for the object detection tasks. It uses IoU (Intersection over Union) threshold such as mAP@0.5 to evaluate whether the predicted box is TP (True Positive), FP (False Positive), or FN (False Negative).</p>
  <p>But I am confused about the role of classification score in this metric since the positive and negative is determined by the IoU, not the classification score. So, what is the role of classification scores in mAP evaluation?</p>
  <p>Let's describe it by example, suppose there is a single object in an image with the ground-truth as follows:</p>
  <ul>
  <li>Bounding boxes: [[100, 100, 200, 200]]</li>
  <li>Class Index: [0]</li>
  </ul>
  <p>Then the prediction of the object detection model resulting as follows:</p>
  <ul>
  <li>Bounding boxes: [[100, 100, 200, 200], [100, 100, 200, 200], [100, 100, 200, 200]]</li>
  <li>Class Indexes: [3, 2, 0]</li>
  <li>Class Scores: [0.9, 0.75, 0.25]</li>
  </ul>
  <p>When I tried to calculate the mAP using this library: <a href="https://pypi.org/project/mapcalc/" rel="nofollow noreferrer">https://pypi.org/project/mapcalc/</a></p>
  <p>The mAP score is 1.0. So I am confused in the mAP point of view, this prediction is calculated as the correct prediction? So what is the role of classification score in this case? Should we also define the classification score threshold when using mAP?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2022-05-05T17:57:44.617Z
Comments.Text: [Confidence Calibration for Object Detection and Segmentation](https://arxiv.org/pdf/2202.12785.pdf)
Comments.UserDisplayName: Santhosh Dhaipule Chandrakanth
Tags: <machine-learning><computer-vision><object-detection><metric>
Title: Role of confidence or classification score in object detection mAP metrics
UserDisplayName: Naufal Suryanto
ThreadText: # Role of confidence or classification score in object detection mAP metrics
  
  (Asked by: username_0 on 2021-08-23)
  
  I know that mAP (mean Average Precision) is the common evaluation metric for the object detection tasks. It uses IoU (Intersection over Union) threshold such as mAP@0.5 to evaluate whether the predicted box is TP (True Positive), FP (False Positive), or FN (False Negative).
  
  But I am confused about the role of classification score in this metric since the positive and negative is determined by the IoU, not the classification score. So, what is the role of classification scores in mAP evaluation?
  
  Let's describe it by example, suppose there is a single object in an image with the ground-truth as follows:
  
  * Bounding boxes: [[100, 100, 200, 200]]
  * Class Index: [0]
  
  Then the prediction of the object detection model resulting as follows:
  
  * Bounding boxes: [[100, 100, 200, 200], [100, 100, 200, 200], [100, 100, 200, 200]]
  * Class Indexes: [3, 2, 0]
  * Class Scores: [0.9, 0.75, 0.25]
  
  When I tried to calculate the mAP using this library: <https://pypi.org/project/mapcalc/>
  
  The mAP score is 1.0. So I am confused in the mAP point of view, this prediction is calculated as the correct prediction? So what is the role of classification score in this case? Should we also define the classification score threshold when using mAP?
  
  **Tags:** machine-learning, computer-vision, object-detection, metric
  
  > **Comments:**
  > **username_1**: [Confidence Calibration for Object Detection and Segmentation](https://arxiv.org/pdf/2202.12785.pdf)  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2022-05-05. Score: 0
  
  Nice question got me thinking, hope these help you understand better [Source](https://alexeyab84.medium.com/hi-jonathan-hui-d2f2baa615ad)
  
  1. MS COCO — uses 101-Recall-points on PR-chart [mAP (mean Average Precision) for Object Detection](https://jonathan-hui.medium.com/map-mean-average-precision-for-object-detection-45c121a31173)
  2. PascalVOC2007 — uses 11-Recall-points on PR-chart
  3. PascalVOC2010–2012 — uses Area-Under-Curve on PR-chart
  4. ImageNet — uses Area-Under-Curve on PR-chart
  
  I too have to understand it further
  
  
  ---
  
  