Which problems can this software solve: Deep learning uses many models, most of which are not yet fully understood. Therefore, there are many research questions which this software could help address. The paper that contains the OpenCV model requires re-training every time the input image or model parameters changes. This makes it difficult to scale to a large number of parameters (which are required by most current deep learning applications). The software solves the re-training problem by providing a tunable version of the OpenCV algorithm. This model also provides a means to bundle the parameters in a manner where training or prediction can be enabled in a programmatic way at run-time.
What is the problem this software solves: Approximately 60% of the existing deep learning efforts in computer vision are currently utilizing small, fixed-size, imaging-based convolution layers in their network architecture. This creates a performance bottleneck, and is a significant obstacle to scaling deep learning technologies to a field such as autonomous mobile robotics that requires a large amount of training data, and an associated network that accommodates an extremely large number of parameters. Deep learning, on the other hand, makes very strong assumptions regarding the uniqueness of each input image, and the high-level semantics it conveys. For computer vision applications, these assumptions are broadly known as “locality.”
How does this software work: The software is a toolbox to allow the construction of deep-learning based computer vision systems using the open-source software library known as OpenCV. It contains pre-trained models which can be easily incorporated into a network architecture, and therefore will allow the efficient training of very large networks without the need to retrain every time we receive a different input image.
Since the release of the Google TensorFlow, a number of deep learning frameworks have been released by different companies/research groups. Each offers an efficient computer vision implementation as standalone packages. The purpose of this toolbox is to assist users in training and deploying deep learning models. Included in the toolbox are the following deep learning frameworks: d2c66b5586