Keras data augmentation with large dataset

#Keras data augmentation with large dataset generator#
#Keras data augmentation with large dataset Offline#

HereвЂ™s an academic paperВ that explains PBA in great detail, describes its implementation and provides access to its source code. PBA treats finding the right augmentation policy as an optimization problem. PBA generates non-stationary augmentation policy schedules instead of a fixed augmentation policy. PBA has recently been gaining popularity as a low cost alternative to GANs and neural style transfer techniques. Many techniques including mattingВ and neural style transferВ have seen significant improvements to achieve this in the recent years. For example, we could replace the background behind the object of interest in one image with that of another image. Mixing images is another advanced image augmentation technique it involves blending parts of two images into one.

#Keras data augmentation with large dataset generator#

Over a period of time, the generator networks learn to create images that are practically indistinguishable from real images. Generator networks try to create вЂњfakeвЂќ images similar to the images in the training set, and the discriminator networks try to distinguish between fake and real data. Here, two networks (a generator and a discriminator) are trying to вЂњfoolвЂќ each other. Unlike random cropping, zoom augmentation is applied to a predefined area of the image which forces the ML model to refine its assumptions about what lies beyond the image boundary.ДЄugmentation using GANs is a fairly new and advanced area of data augmentation.

Zoom augmentation simply scales an image up or down. Online augmentation, sometimes referred to as real-time augmentation, does not require saving augmented data to the disk, negating the need for increased storage, making it ideal for larger training datasets. Here, instead of storing the augmented data in the training dataset, the transformations are done on mini-batches that are then fed to the ML model during training.

#Keras data augmentation with large dataset Offline#

Thus, offline augmentation is generally preferred for relatively smaller datasets. For example, if we simply rotate all the images by a predefined angle once, we have doubled the dataset size.

However, this also significantly increases the storage needs. Offline augmentation, sometimes referred to as pre-processing augmentation, is easy to understand, visualize and control as the artificial data is created beforehand. Here, we perform data augmentation on the training dataset before we train the ML model. Data augmentation can be broadly classified into two вЂњtypesвЂќ depending on where in the ML pipeline it occurs.