RStudio AI Blog: Training ImageNet with R

ImageNet (Deng et al. 2009) is an image database organized according to the WordNet (Miller 1995) hierarchy which, historically, has been used in computer vision benchmarks and research. However, it was not until AlexNet (Krizhevsky, Sutskever, and Hinton 2012) demonstrated the efficiency of deep learning using convolutional neural networks on GPUs that the computer-vision discipline turned to deep learning to achieve state-of-the-art models that revolutionized their field. Given the importance of ImageNet and AlexNet, this post introduces tools and techniques to consider when training ImageNet and other large-scale datasets with R. Now, in order to process ImageNet, we will first have to divide and conquer, partitioning the dataset into several manageable subsets. Afterwards, we will train ImageNet using AlexNet across multiple GPUs and compute instances. Preprocessing ImageNet and distributed training are the two topics that this post will present and discuss, starting with preprocessing ImageNet. When dealing with large datasets, even simple tasks like downloading or reading a dataset can be much harder than what you would expect.