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What does image augmentation do?
Image augmentation is a technique of altering the existing data to create some more data for the model training process. In other words, it is the process of artificially expanding the available dataset for training a deep learning model.
Why is data augmentation used?
Data augmentation is a strategy that enables practitioners to significantly increase the diversity of data available for training models, without actually collecting new data. Data augmentation techniques such as cropping, padding, and horizontal flipping are commonly used to train large neural networks.
How does data augmentation help you achieve this?
Data Augmentation helps you to achieve this. Image Augmentation is one of the technique we can apply on an image dataset to expand our dataset so that no overfitting occurs and our model generalizes well. So, If you have relatively small dataset then go with this technique to expand your dataset to generalize your model.
What does transform mean in image data augmentation?
Transforms include a range of operations from the field of image manipulation, such as shifts, flips, zooms, and much more. The intent is to expand the training dataset with new, plausible examples. This means, variations of the training set images that are likely to be seen by the model.
When do you use data augmentation in keras?
Let’s examine the most trivial case where you only have one image and you want to apply data augmentation to create an entire dataset of images, all based on that one image. To accomplish this task, you would: Load the original input image from disk. Randomly transform the original image via a series of random translations, rotations, etc.
How is the imagedatagenerator different from data augmentation?
It’s not taking the original data, randomly transforming it, and then returning both the original data and transformed data. Instead, the ImageDataGenerator accepts the original data, randomly transforms it, and returns only the new, transformed data. But remember how I said this was a trick question?