Answers.Body: <p>It should be our hope that ML can find patterns in otherwise noisy or imperfect data.  Unfortunately, some training examples may only confuse an algorithm but with any luck, dicey examples simply have a low confidence.</p>
  <p>It is a practitioners job to give true labels.  The dataset may later need to be curated to get optimal performance.  But a good heuristic is likely to start with a fully and correctly annotated dataset and go from there.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2024-08-29T12:56:20.293Z
Answers.LastActivityDate: 2024-08-29T12:56:20.293Z
Answers.UserDisplayName: foreverska
Body: <p>I am training my YOLO to detect airplanes and drones. in some of the pictures it is impossible to detect that the object is indeed an airplane, and it even looks like a drone (pictures are taken from very far away), but I know from the context that it does. should I still label it as an airplane?</p>
  
Tags: <machine-learning><training><python><yolo><training-datasets>
Title: Labeling policy for airplane detecting YOLO
UserDisplayName: Or Perez
ThreadText: # Labeling policy for airplane detecting YOLO
  
  (Asked by: username_0 on 2024-08-29)
  
  I am training my YOLO to detect airplanes and drones. in some of the pictures it is impossible to detect that the object is indeed an airplane, and it even looks like a drone (pictures are taken from very far away), but I know from the context that it does. should I still label it as an airplane?
  
  **Tags:** machine-learning, training, python, yolo, training-datasets
  
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  ## 1 answers
  
  ### Answer by username_1 on 2024-08-29. Score: 0
  
  It should be our hope that ML can find patterns in otherwise noisy or imperfect data. Unfortunately, some training examples may only confuse an algorithm but with any luck, dicey examples simply have a low confidence.
  
  It is a practitioners job to give true labels. The dataset may later need to be curated to get optimal performance. But a good heuristic is likely to start with a fully and correctly annotated dataset and go from there.
  
  
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