Answers.Body: <p><strong>APsmall</strong> stands for <strong>Average Precision for small objects</strong>.</p>
  <hr />
  <p>This website contains all the descriptions about the metrics:
  <a href="https://cocodataset.org/#detection-eval" rel="nofollow noreferrer">https://cocodataset.org/#detection-eval</a></p>
  <p>If you want to know more about the metrics evaluation (how to calculate this metric), you can find the metric's source code here: <a href="https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocotools/cocoeval.py" rel="nofollow noreferrer">https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocotools/cocoeval.py</a></p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2021-08-17T19:42:55.510Z
Answers.LastActivityDate: 2021-08-17T19:42:55.510Z
Answers.UserDisplayName: Andre Goulart
Answers.Body: <blockquote>
  <p>Does it mean that there are no small objects in my dataset to be detected?</p>
  </blockquote>
  <p>Yes.</p>
  <p>From the evaluation source code as mentioned by the other answer, <a href="https://github.com/cocodataset/cocoapi/blob/8c9bcc3cf640524c4c20a9c40e89cb6a2f2fa0e9/PythonAPI/pycocotools/cocoeval.py#L334" rel="nofollow noreferrer">L334</a>:</p>
  <blockquote>
  <p>-1 for the precision of absent categories</p>
  </blockquote>
  <p>From the coco dataset website, <a href="https://cocodataset.org/#detection-eval" rel="nofollow noreferrer">Detection Evaluation</a>:</p>
  <blockquote>
  <p><span class="math-container">$AP_{small}$</span>: AP for small objects: area &lt; <span class="math-container">$32^{2}$</span><br />
  <span class="math-container">$AP_{medium}$</span>: AP for medium objects: <span class="math-container">$32^{2}$</span> &lt; area &lt; <span class="math-container">$96^{2}$</span><br />
  <span class="math-container">$AP_{large}$</span>: AP for large objects: area &gt; <span class="math-container">$96^{2}$</span></p>
  </blockquote>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2023-08-30T19:59:45.870Z
Answers.LastActivityDate: 2023-08-30T19:59:45.870Z
Answers.UserDisplayName: IndefiniteBen
Body: <p>I am training some Object-Detection-Models from the TensorFlow Object Detection API and got from the evaluation with MS COCO metrics the following results for Average Precision:</p>
  <p>IoU = 0.5;0.9
  maxDets = 100
  area = small
  AP = -1.000</p>
  <p>The other values all make sense to me. But I don´t know what the -1.000 stands for. Does it mean that there are no small objects in my dataset to be detected?</p>
  
Tags: <deep-learning><convolutional-neural-networks><object-detection><testing><precision>
Title: What does a value of -1.000 mean in MS COCO Metrics for Object Detection
UserDisplayName: Benjamin Stecker
ThreadText: # What does a value of -1.000 mean in MS COCO Metrics for Object Detection
  
  (Asked by: username_0 on 2021-08-17)
  
  I am training some Object-Detection-Models from the TensorFlow Object Detection API and got from the evaluation with MS COCO metrics the following results for Average Precision:
  
  IoU = 0.5;0.9
  maxDets = 100
  area = small
  AP = -1.000
  
  The other values all make sense to me. But I don´t know what the -1.000 stands for. Does it mean that there are no small objects in my dataset to be detected?
  
  **Tags:** deep-learning, convolutional-neural-networks, object-detection, testing, precision
  
  ---
  
  ## 2 answers
  
  ### Answer by username_1 on 2021-08-17. Score: 0
  
  **APsmall** stands for **Average Precision for small objects**.
  
  ---
  
  This website contains all the descriptions about the metrics:
  <https://cocodataset.org/#detection-eval>
  
  If you want to know more about the metrics evaluation (how to calculate this metric), you can find the metric's source code here: <https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocotools/cocoeval.py>
  
  
  ---
  
  ### Answer by username_2 on 2023-08-30. Score: 0
  
  > Does it mean that there are no small objects in my dataset to be detected?
  
  Yes.
  
  From the evaluation source code as mentioned by the other answer, [L334](https://github.com/cocodataset/cocoapi/blob/8c9bcc3cf640524c4c20a9c40e89cb6a2f2fa0e9/PythonAPI/pycocotools/cocoeval.py#L334):
  
  > -1 for the precision of absent categories
  
  From the coco dataset website, [Detection Evaluation](https://cocodataset.org/#detection-eval):
  
  > $AP\_{small}$: AP for small objects: area < $32^{2}$  
  > $AP\_{medium}$: AP for medium objects: $32^{2}$ < area < $96^{2}$  
  > $AP\_{large}$: AP for large objects: area > $96^{2}$
  
  
  ---
  
  