Large-scale object recognition with CUDA-accelerated hierarchical neural networks

Robust recognition of arbitrary object classes in natural visual scenes is an aspiring goal with numerous practical applications, for instance, in the area of autonomous robotics and autonomous vehicles. One obstacle on the way towards human-like recognition performance is the limitation of computational power, restricting the size of the training and testing dataset as well as the complexity of the object recognition system. In this work, we present a hierarchical, locally-connected neural network model that is well-suited for large-scale, high-performance object recognition. By using the NVIDIA CUDA framework, we create a massively parallel implementation of the model which is executed on a state-of-the-art graphics card. This implementation is up to 82 times faster than a single-core CPU version of the system. This significant gain in computational performance allows us to evaluate the model on a very large, realistic, and challenging set of natural images which we extracted from the LabelMe dataset. To compare our model to other approaches, we also evaluate the recognition performance using the well-known MNIST and NORB datasets, achieving a testing error rate of 0.76% and 2.87%, respectively.