What is data augmentation?
Data augmentation is a technique used to increase the diversity of the available data without collecting new data. It enlarges our dataset by making small changes in it.
We can increase data diversity by using the following data augmentation techniques:
1)Cropping - just crops the image
2)Padding - similar to cropping but the size of the image remains the same
3)Flipping - flip the images in three different ways vertical, horizontal and vertical + horizontal
4)Rotating - rotates the image for a certain degree
5)Combining - combine two different images vertically or horizontally.
Why data augmentation is used?
Less data to train AI model results in less accurate and inefficient AI models. For example: take a look at this images.
All the image contains the face of Santa Claus, but for poorly trained model all these are unique images. We can collect only limited pictures under limited condition but there are lot more in real world and what if our model even can't identify the same pictures which is in different angle or color or surrounding. So for that purpose we use data augmentation we can create variety of data with the available data.
Demo for data augmentation:
For more details check out this link
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