Last Updated on July 5, 2019 The pixel values in images must be scaled prior to providing the images as input to a deep learning neural network model during the training or evaluation of the model. Traditionally, the images would have to be scaled prior to the development of the model and stored in memory or on disk in the scaled format. An alternative approach is to scale the images using a preferred scaling technique just-in-time during the training or […]

Last Updated on September 12, 2019 Color images have height, width, and color channel dimensions. When represented as three-dimensional arrays, the channel dimension for the image data is last by default, but may be moved to be the first dimension, often for performance-tuning reasons. The use of these two “channel ordering formats” and preparing data to meet a specific preferred channel ordering can be confusing to beginners. In this tutorial, you will discover channel ordering formats, how to prepare and […]

Last Updated on July 5, 2019 It can be convenient to use a standard computer vision dataset when getting started with deep learning methods for computer vision. Standard datasets are often well understood, small, and easy to load. They can provide the basis for testing techniques and reproducing results in order to build confidence with libraries and methods. In this tutorial, you will discover the standard computer vision datasets provided with the Keras deep learning library. After completing this tutorial, […]

Last Updated on April 2, 2020 Deep Learning for Computer Vision Crash Course.Bring Deep Learning Methods to Your Computer Vision Project in 7 Days. We are awash in digital images from photos, videos, Instagram, YouTube, and increasingly live video streams. Working with image data is hard as it requires drawing upon knowledge from diverse domains such as digital signal processing, machine learning, statistical methods, and these days, deep learning. Deep learning methods are out-competing the classical and statistical methods on […]

Last Updated on July 5, 2019 There are conventions for storing and structuring your image dataset on disk in order to make it fast and efficient to load and when training and evaluating deep learning models. Once structured, you can use tools like the ImageDataGenerator class in the Keras deep learning library to automatically load your train, test, and validation datasets. In addition, the generator will progressively load the images in your dataset, allowing you to work with both small […]

Last Updated on July 5, 2019 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. 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. The Keras deep […]

Last Updated on April 3, 2020 Data augmentation is a technique often used to improve performance and reduce generalization error when training neural network models for computer vision problems. The image data augmentation technique can also be applied when making predictions with a fit model in order to allow the model to make predictions for multiple different versions of each image in the test dataset. The predictions on the augmented images can be averaged, which can result in better predictive […]

Last Updated on April 17, 2020 Convolutional layers are the major building blocks used in convolutional neural networks. A convolution is the simple application of a filter to an input that results in an activation. Repeated application of the same filter to an input results in a map of activations called a feature map, indicating the locations and strength of a detected feature in an input, such as an image. The innovation of convolutional neural networks is the ability to […]

Last Updated on August 16, 2019 The convolutional layer in convolutional neural networks systematically applies filters to an input and creates output feature maps. Although the convolutional layer is very simple, it is capable of achieving sophisticated and impressive results. Nevertheless, it can be challenging to develop an intuition for how the shape of the filters impacts the shape of the output feature map and how related configuration hyperparameters such as padding and stride should be configured. In this tutorial, […]

Last Updated on July 5, 2019 Convolutional layers in a convolutional neural network summarize the presence of features in an input image. A problem with the output feature maps is that they are sensitive to the location of the features in the input. One approach to address this sensitivity is to down sample the feature maps. This has the effect of making the resulting down sampled feature maps more robust to changes in the position of the feature in the […]