Best Practices for Preparing and Augmenting Image Data for Convolutional Neural Networks

It is challenging to know how to best prepare image data when training a convolutional neural network. This involves both scaling the pixel values and use of augmentation techniques during both the training and evaluation of the model. Instead of testing a wide range of options, a useful shortcut is to consider the types of data preparation, train-time augmentation, and test-time augmentation used by state-of-the-art models that notably achieve the best performance on a challenging computer vision dataset, namely the Large Scale Visual Recognition Challenge, or ILSVRC, that uses the ImageNet dataset. In this tutorial, you will discover best practices for preparing and augmenting photographs for image classification tasks with convolutional neural networks. Best Practices for Preparing and Augmenting Image Data for Convolutional Neural Networks Photo by Mark in New Zealand, some rights reserved.