<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>

CC BY-SA 4.0
2024-07-20T17:39:46.480Z
2024-07-20T17:39:46.480Z
Gabriel Ribeiro C.
<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>

<deep-learning><generative-adversarial-networks><data-augmentation>
Image Augmentation for Leaf Disease Detection: Training or Testing?
Dawood Ahmad
# 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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