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.īest Practices for Preparing and Augmenting Image Data for Convolutional Neural Networks Test-time augmentation should probably involve both a mixture of multiple rescaling of each image as well as predictions for multiple different systematic crops of each rescaled version of the image.Training data augmentation should probably involve random rescaling, horizontal flips, perturbations to brightness, contrast, and color, as well as random cropping.Image data should probably be centered by subtracting the per-channel mean pixel values calculated on the training dataset.In this tutorial, you will discover best practices for preparing and augmenting photographs for image classification tasks with convolutional neural networks.Īfter completing this tutorial, you will know: 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. This involves both scaling the pixel values and use of image data augmentation techniques during both the training and evaluation of the model. It is challenging to know how to best prepare image data when training a convolutional neural network.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |