What are the best practices for data augmentation in Python machine learning?

What are the best practices for data augmentation in Python machine learning?

Some best practices include using libraries like Keras and Albumentations, ensuring diversity in transformations, and validating augmented data.

Can you give examples of effective augmentation techniques?

Sure! Techniques like rotation, flipping, zooming, and color adjustments are commonly used to enhance datasets.

How do I implement these techniques in my code?

You can use the ImageDataGenerator class in Keras or the transforms module in PyTorch to easily apply these techniques.

What should I be cautious about when using data augmentation?

Be careful not to over-augment, as it can lead to unrealistic data. Always validate the performance on a separate test set.