Body: <p>Assume that a CNN model is to be developed to recognize commercial domestic planes flying in the sky. The training data should include images of flying domestic planes for true positives. Additionally, it should encompass other types of aircrafts, such as private jets and helicopters. Should the training data also include instances with no flying aircraft so that the output layer has two outputs: Domestic plane and Non-domestic plane? Or would it be better to have three outputs in the output layer: Commercial domestic plane, Non-commercial domestic, Non flying aircraft?</p>
  <p>Cheers</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-05-01T05:20:07.440Z
Comments.Text: I believe this is rather similar to pros and cons of training a 2 output classification network [tiger, mouse] and training a 3 output classification network [male-tiger, female-tiger, mouse]. Procedure-wise the training steps are identical (using labeled 2 or 3 category input-output pairs). But the [male-tiger, female-tiger, mouse] may have poorer accuracy if it is hard to distinguish male-tiger vs female-tiger.
Comments.UserDisplayName: James
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-05-02T04:16:26.057Z
Comments.Text: If there's no strict condition on you developing this yourself, then I'd recommend looking for ready-made solutions online. I've found that lots of people will develop models for somewhat common problems like this and put them on, say, GitHub.
Comments.UserDisplayName: Tom
Tags: <deep-learning><convolutional-neural-networks>
Title: CNN model configuration: advice
UserDisplayName: KM23
ThreadText: # CNN model configuration: advice
  
  (Asked by: username_0 on 2024-05-01)
  
  Assume that a CNN model is to be developed to recognize commercial domestic planes flying in the sky. The training data should include images of flying domestic planes for true positives. Additionally, it should encompass other types of aircrafts, such as private jets and helicopters. Should the training data also include instances with no flying aircraft so that the output layer has two outputs: Domestic plane and Non-domestic plane? Or would it be better to have three outputs in the output layer: Commercial domestic plane, Non-commercial domestic, Non flying aircraft?
  
  Cheers
  
  **Tags:** deep-learning, convolutional-neural-networks
  
  > **Comments:**
  > **username_1**: I believe this is rather similar to pros and cons of training a 2 output classification network [tiger, mouse] and training a 3 output classification network [male-tiger, female-tiger, mouse]. Procedure-wise the training steps are identical (using labeled 2 or 3 category input-output pairs). But the [male-tiger, female-tiger, mouse] may have poorer accuracy if it is hard to distinguish male-tiger vs female-tiger.  
  > **username_2**: If there's no strict condition on you developing this yourself, then I'd recommend looking for ready-made solutions online. I've found that lots of people will develop models for somewhat common problems like this and put them on, say, GitHub.  
  
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  ## 0 answers
  
  