What are the common challenges in data augmentation?

What are the common challenges in data augmentation?

Some common challenges include overfitting, maintaining label consistency, and computational costs.

How can overfitting be avoided during data augmentation?

To avoid overfitting, you can use techniques like dropout, data shuffling, and ensuring a diverse augmentation strategy.

What tools can help with data augmentation?

Popular tools include TensorFlow, Keras, and Augmentor, which provide various augmentation techniques.

Can data augmentation improve model performance?

Yes, it can enhance model performance by providing more diverse training data, helping the model generalize better.