Body: <p><br />
  I want to develop a CNN model to identify 24 hand signs in American Sign Language. I created a custom <a href="http://www.kaggle.com/dataset/04322e12909662a11f5154857a749b54b389b77bd99f5bd4c3e0db0ec34abe68" rel="nofollow noreferrer">dataset</a> that contains 3000 images for each hand sign i.e. 72000 images in the entire dataset.
  <br /><br />
  For training the model, I would be using 80-20 dataset split (2400 images/hand sign in the training set and 600 images/hand sign in the validation set). My question is:<br />
  Should I randomly shuffle the images when creating the dataset? And Why?</p>
  
  <p><br />PS: Based on my previous experience, it led to validation loss being lower than training loss and validation accuracy more than training accuracy.</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-04-13T21:18:53.360Z
Comments.Text: The answer is: Yes, you should shuffle the dataset, for an explanation check think [link](https://datascience.stackexchange.com/questions/24511/why-should-the-data-be-shuffled-for-machine-learning-tasks). About the higher validation/training loss: is a consistent over several training trials? Also, is it consistent over several epochs or does it happened only at the beginning of each training?
Comments.UserDisplayName: Edoardo Guerriero
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-04-13T21:57:49.780Z
Comments.Text: The validation loss was low throughout the training phase. You can check it here= https://ai.stackexchange.com/questions/18551/validation-loss-is-lower-than-training-loss
Comments.UserDisplayName: mayuresh_sa
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-04-14T23:40:05.443Z
Comments.Text: @EdoardoGuerriero That looks a lot like an answer rather than a comment. Please consider expanding it to an answer, and adding a little extra detail about what's in the link.
Comments.UserDisplayName: John Doucette
Tags: <neural-networks><machine-learning><deep-learning><convolutional-neural-networks><computer-vision>
Title: Creating Dataset for Image Classification
UserDisplayName: mayuresh_sa
ThreadText: # Creating Dataset for Image Classification
  
  (Asked by: username_0 on 2020-04-13)
  
  I want to develop a CNN model to identify 24 hand signs in American Sign Language. I created a custom [dataset](http://www.kaggle.com/dataset/04322e12909662a11f5154857a749b54b389b77bd99f5bd4c3e0db0ec34abe68) that contains 3000 images for each hand sign i.e. 72000 images in the entire dataset.
    
    
  For training the model, I would be using 80-20 dataset split (2400 images/hand sign in the training set and 600 images/hand sign in the validation set). My question is:  
  Should I randomly shuffle the images when creating the dataset? And Why?
  
  PS: Based on my previous experience, it led to validation loss being lower than training loss and validation accuracy more than training accuracy.
  
  **Tags:** neural-networks, machine-learning, deep-learning, convolutional-neural-networks, computer-vision
  
  > **Comments:**
  > **username_1**: The answer is: Yes, you should shuffle the dataset, for an explanation check think [link](https://datascience.stackexchange.com/questions/24511/why-should-the-data-be-shuffled-for-machine-learning-tasks). About the higher validation/training loss: is a consistent over several training trials? Also, is it consistent over several epochs or does it happened only at the beginning of each training?  
  > **username_0**: The validation loss was low throughout the training phase. You can check it here= https://ai.stackexchange.com/questions/18551/validation-loss-is-lower-than-training-loss  
  > **username_2**: @username_1 That looks a lot like an answer rather than a comment. Please consider expanding it to an answer, and adding a little extra detail about what's in the link.  
  
  ---
  
  ## 0 answers
  
  