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 […]
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 […]
Once you fit a deep learning neural network model, you must evaluate its performance on a test dataset. This is critical, as the reported performance allows you to both choose between candidate models and to communicate to stakeholders about how good the model is at solving the problem. The Keras deep learning API model is […]
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 […]
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 […]
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 […]
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 […]
The Stanford course on deep learning for computer vision is perhaps the most widely known course on the topic. This is not surprising given that the course has been running for four years, is presented by top academics and researchers in the field, and the course lectures and notes are made freely available. This is […]
Computer Vision, often abbreviated as CV, is defined as a field of study that seeks to develop techniques to help computers “see” and understand the content of digital images such as photographs and videos. The problem of computer vision appears simple because it is trivially solved by people, even very young children. Nevertheless, it largely […]
Andrew Ng is famous for his Stanford machine learning course provided on Coursera. In 2017, he released a five-part course on deep learning also on Coursera titled “Deep Learning Specialization” that included one module on deep learning for computer vision titled “Convolutional Neural Networks.” This course provides an excellent introduction to deep learning methods for […]
