What are the different image augmentation techniques and how do they compare?

What are the different image augmentation techniques and how do they compare?

Image augmentation techniques include rotation, flipping, scaling, cropping, and color adjustment. Each technique has its strengths. For instance, rotation can help with orientation variance, while color adjustment can enhance model robustness to lighting changes.

Which technique is the most effective for improving model accuracy?

The effectiveness of a technique often depends on the specific dataset and task. However, a combination of techniques usually yields the best results, as it helps the model generalize better.

Can you give examples of when to use each technique?

Sure! Use rotation for images with varying orientations, flipping for symmetrical objects, scaling for different sizes, cropping to focus on specific areas, and color adjustment for varying lighting conditions.

Are there any downsides to using these techniques?

Yes, over-augmentation can lead to unrealistic images that may confuse the model. It's important to balance augmentation with the original data.