What are the best practices in data augmentation?

What are the best practices in data augmentation?

Data augmentation involves techniques to increase the diversity of your training dataset. Best practices include using transformations like rotation, flipping, scaling, and color adjustments.

Can you give me examples of specific techniques?

Sure! Common techniques include random cropping, adding noise, and applying geometric transformations. Each technique helps improve model robustness.

How do I choose the right techniques for my dataset?

Choosing the right techniques depends on your dataset's characteristics and the specific task. Experimentation and validation are key.

Are there any tools or libraries you recommend for data augmentation?

Yes! Libraries like Keras, Albumentations, and Augmentor are great for implementing data augmentation easily.