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Software Frameworks and Toolsets for Deep Learning-based Vision Processing

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This article provides both background and implementation-detailed information on software frameworks and toolsets for deep learning-based vision processing, an increasingly popular and robust alternative to classical computer vision algorithms. It covers the leading available software framework options, the root reasons for their abundance, and guidelines for selecting an optimal approach among the candidates for a particular implementation. It also covers "middleware" utilities that optimize a generic framework for use in a particular embedded implementation, comprehending factors such as applicable data types and bit widths, as well as available heterogeneous computing resources.

For developers in specific markets and applications, toolsets that incorporate deep learning techniques can provide an attractive alternative to an intermediary software framework-based development approach. And the article also introduces an industry alliance available to help product creators optimally implement deep learning-based vision processing in their hardware and software designs.

Traditionally, computer vision applications have relied on special-purpose algorithms that are painstakingly designed to recognize specific types of objects. Recently, however, CNNs (convolutional neural networks) and other deep learning approaches have been shown to be superior to traditional algorithms on a variety of image understanding tasks. In contrast to traditional algorithms, deep learning approaches are generalized learning algorithms trained through examples to recognize specific classes of objects, for example, or to estimate optical flow. Since deep learning is a comparatively new approach, however, the usage expertise for it in the developer community is comparatively immature versus with traditional algorithms such as those included in the OpenCV open-source computer vision library.

General-purpose deep learning software frameworks can significantly assist both in getting developers up to speed and in getting deep learning-based designs completed in a timely and robust manner, as can deep learning-based toolsets focused on specific applications. However, when using them, it's important to keep in mind that the abundance of resources that may be assumed in a framework originally intended for PC-based software development, for example, aren't likely also available in an embedded implementation. Embedded designs are also increasingly heterogeneous in nature, containing multiple computing nodes (not only a CPU but also GPU, FPGA, DSP and/or specialized co-processors);...