Body: <p>I am training a deep learning model for object detection. The consensus is that the more images that you have, the better the results will be. All the tutorials that I have seen say that more images are key.</p>
  <p>I am labeling objects in my images with Label-Img, which provides the algorithm with specific training samples on the images. For my images, I am using photos with dimensions of 1100 x 1100 pixels. In my case, I could generate anywhere between 50-100 high-quality training samples per image. For example:</p>
  <p><a href="https://i.sstatic.net/DXCOm.jpg" rel="nofollow noreferrer"><img src="https://i.sstatic.net/DXCOm.jpg" alt="enter image description here" /></a></p>
  <p>In cases such as this where large numbers of training samples can be generated from a single image, do you really need several hundred images? Or can you lessen the number of images because of the number of training samples?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-11-07T10:27:14.173Z
Comments.Text: The short answer could be "yes, if a single image contains several instances of the object, that made thinks easier and number of training images can be reduced". However, for a good answer, could you describe what kind of NN network you use ?
Comments.UserDisplayName: pasaba por aqui
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-11-08T21:20:07.787Z
Comments.Text: @pasabaporaqui Convolutional Neural Networks (i.e. ResNet50) and RCNNs.
Comments.UserDisplayName: sneeze_shiny
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-07-15T23:20:29.697Z
Comments.Text: @ihb Do you already find the answer by yourself? If not, what is your input image dimension to the first layer of neural network and what is your kernel size? How about these suggestions,  https://datascience.stackexchange.com/questions/11390/any-rules-of-thumb-on-number-of-features-versus-number-of-instances-small-da and            https://stats.stackexchange.com/questions/10423/number-of-features-vs-number-of-observations
Comments.UserDisplayName: Cloud Cho
Tags: <deep-learning><training><object-detection>
Title: When training deep learning models for object detection in images, do you need a large number of images, or a large number of training samples?
UserDisplayName: sneeze_shiny
ThreadText: # When training deep learning models for object detection in images, do you need a large number of images, or a large number of training samples?
  
  (Asked by: username_0 on 2020-11-06)
  
  I am training a deep learning model for object detection. The consensus is that the more images that you have, the better the results will be. All the tutorials that I have seen say that more images are key.
  
  I am labeling objects in my images with Label-Img, which provides the algorithm with specific training samples on the images. For my images, I am using photos with dimensions of 1100 x 1100 pixels. In my case, I could generate anywhere between 50-100 high-quality training samples per image. For example:
  
  [![enter image description here](https://i.sstatic.net/DXCOm.jpg)](https://i.sstatic.net/DXCOm.jpg)
  
  In cases such as this where large numbers of training samples can be generated from a single image, do you really need several hundred images? Or can you lessen the number of images because of the number of training samples?
  
  **Tags:** deep-learning, training, object-detection
  
  > **Comments:**
  > **username_1**: The short answer could be "yes, if a single image contains several instances of the object, that made thinks easier and number of training images can be reduced". However, for a good answer, could you describe what kind of NN network you use ?  
  > **username_0**: @username_1 Convolutional Neural Networks (i.e. ResNet50) and RCNNs.  
  > **username_2**: @ihb Do you already find the answer by yourself? If not, what is your input image dimension to the first layer of neural network and what is your kernel size? How about these suggestions,  https://datascience.stackexchange.com/questions/11390/any-rules-of-thumb-on-number-of-features-versus-number-of-instances-small-da and            https://stats.stackexchange.com/questions/10423/number-of-features-vs-number-of-observations  
  
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  ## 0 answers
  
  