Caffe provides multimedia scientists and practitioners with a clean and modiable framework for state-of-the-art deep learning algorithms and a collection of reference models. The framework is a BSD-licensed C++ library with Python and MATLAB binding
Existing deep convolutional neural networks (CNNs) require a fixed-size (e.g., 224224) input image. This requirement is “artificial” and may reduce the recognition accuracy for the images or sub-images of an arbitrary size/scale. In this work, we eq
Intelligent tasks, such as visual perception, auditory perception, and language understanding require the construction of good internal representations of the world (or ”features”), which must be invariant to irrelevant variations of the input while
Large Convolutional Network models have recently demonstrated impressive classication performance on the ImageNet benchmark (Krizhevsky et al., 2012). However there is no clear understanding of why they perform so well, or how they might be improved
