Answers.Body: <p>From my personal knowledge, you should only ever augment the training subset. The idea behind a test subset is to use it to evaluate the model's generalization capability/performance (i.e. it tries to imitate &quot;real&quot; data).</p>
  <p>By using augmentation methods to increase the size of your test subset you're potentially creating a data leakage. This will cause an overestimation of the model's performance (i.e. results will look better than they actually are).</p>
  <p>This knowledge extends to the Cross-validation training-test folds as well.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2024-07-20T17:39:46.480Z
Answers.LastActivityDate: 2024-07-20T17:39:46.480Z
Answers.UserDisplayName: Gabriel Ribeiro C.
Body: <p>I am working on a leaf disease detection project and evaluating different strategies for augmenting the existing dataset to improve model performance. However, I am facing some confusion. Should I augment only the training dataset, or should I also augment the test dataset?</p>
  <p>For instance, I split the dataset into 5 folds for cross-validation and use a Generative Adversarial Network (GAN) for synthetic data generation. When using folds 1, 2, 3, and 4 for training and fold 5 for testing, should I augment fold 5 as well?</p>
  
Tags: <deep-learning><generative-adversarial-networks><data-augmentation>
Title: Image Augmentation for Leaf Disease Detection: Training or Testing?
UserDisplayName: Dawood Ahmad
ThreadText: # Image Augmentation for Leaf Disease Detection: Training or Testing?
  
  (Asked by: username_0 on 2024-07-11)
  
  I am working on a leaf disease detection project and evaluating different strategies for augmenting the existing dataset to improve model performance. However, I am facing some confusion. Should I augment only the training dataset, or should I also augment the test dataset?
  
  For instance, I split the dataset into 5 folds for cross-validation and use a Generative Adversarial Network (GAN) for synthetic data generation. When using folds 1, 2, 3, and 4 for training and fold 5 for testing, should I augment fold 5 as well?
  
  **Tags:** deep-learning, generative-adversarial-networks, data-augmentation
  
  
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  ## 1 answers
  
  ### Answer by username_1 on 2024-07-20. Score: 1
  
  From my personal knowledge, you should only ever augment the training subset. The idea behind a test subset is to use it to evaluate the model's generalization capability/performance (i.e. it tries to imitate "real" data).
  
  By using augmentation methods to increase the size of your test subset you're potentially creating a data leakage. This will cause an overestimation of the model's performance (i.e. results will look better than they actually are).
  
  This knowledge extends to the Cross-validation training-test folds as well.
  
  
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