Data Augmentation in Python - Innovature

Deep Learning models have made incredible progress in discriminative tasks. This has been fueled by the advancement of deep network architectures, powerful computation, and access to big data. Having a large dataset is crucial for the performance of the deep learning model. Thus with data augmentation we can improve the performance of the model with the data we already have. 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.