![]() Update Jun/2019: Fixed small typo in API example (thanks Georgios).Update May/2019: Fixed data type for pixel values when plotting.Kick-start your project with my new book Deep Learning for Computer Vision, including step-by-step tutorials and the Python source code files for all examples. How to use shift, flip, brightness, and zoom image data augmentation.Image data augmentation is supported in the Keras deep learning library via the ImageDataGenerator class.Image data augmentation is used to expand the training dataset in order to improve the performance and ability of the model to generalize.In this tutorial, you will discover how to use image data augmentation when training deep learning neural networks.Īfter completing this tutorial, you will know: The Keras deep learning neural network library provides the capability to fit models using image data augmentation via the ImageDataGenerator class. ![]() Training deep learning neural network models on more data can result in more skillful models, and the augmentation techniques can create variations of the images that can improve the ability of the fit models to generalize what they have learned to new images. Image data augmentation is a technique that can be used to artificially expand the size of a training dataset by creating modified versions of images in the dataset.
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