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What are common mistakes to avoid with Image Augmentation Techniques

Intermediate

Answer

A common mistake with Image Augmentation Techniques is applying it without fully understanding the underlying data or problem, which often leads to weak or misleading results. Skipping proper testing before relying on it in a real project is another frequent error.
Real-world example A team training a plant disease detector uses image augmentation to rotate and flip leaf photos, helping the model recognize diseased leaves from any angle.

Common follow-ups: What is Computer Vision, How is Image Augmentation Techniques evaluated in practice, What tools are commonly used for Image Augmentation Techniques