What are some common mistakes in data augmentation?

What are some common mistakes in data augmentation?

Some common mistakes include over-augmentation, not validating augmented data, and ignoring class imbalance.

Can you explain what over-augmentation means?

Over-augmentation occurs when too many transformations are applied, leading to unrealistic data that can confuse the model.

How can I validate my augmented data?

You can validate augmented data by checking its performance on a validation set and ensuring it reflects real-world scenarios.

What transformations should I avoid?

Avoid transformations that distort the data too much, such as excessive rotation or scaling, which can misrepresent the original data.