Last Updated on September 12, 2019 Before you can develop predictive models for image data, you must learn how to load and manipulate images and photographs. The most popular and de facto standard library in Python for loading and working with image data is Pillow. Pillow is an updated version of the Python Image Library, or PIL, and supports a range of simple and sophisticated image manipulation functionality. It is also the basis for simple image support in other Python […]

Last Updated on July 5, 2019 Images are comprised of matrices of pixel values. Black and white images are single matrix of pixels, whereas color images have a separate array of pixel values for each color channel, such as red, green, and blue. Pixel values are often unsigned integers in the range between 0 and 255. Although these pixel values can be presented directly to neural network models in their raw format, this can result in challenges during modeling, such […]

Last Updated on July 5, 2019 The promise of deep learning in the field of computer vision is better performance by models that may require more data but less digital signal processing expertise to train and operate. There is a lot of hype and large claims around deep learning methods, but beyond the hype, deep learning methods are achieving state-of-the-art results on challenging problems. Notably, on computer vision tasks such as image classification, object recognition, and face detection. In this […]

Last Updated on August 28, 2020 Image data must be prepared before it can be used as the basis for modeling in image classification tasks. One aspect of preparing image data is scaling pixel values, such as normalizing the values to the range 0-1, centering, standardization, and more. How do you choose a good, or even best, pixel scaling method for your image classification or computer vision modeling task? In this tutorial, you will discover how to choose a pixel […]

Last Updated on July 5, 2019 The Keras deep learning library provides a sophisticated API for loading, preparing, and augmenting image data. Also included in the API are some undocumented functions that allow you to quickly and easily load, convert, and save image files. These functions can be convenient when getting started on a computer vision deep learning project, allowing you to use the same Keras API initially to inspect and handle image data. In this tutorial, you will discover […]

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 […]