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Convolutional neural network (CNN) approaches available in the currentliterature are designed to work primarily with low-resolution images. Whenapplied on very large images, challenges related to GPU memory, smallerreceptive field than needed for semantic correspondence and the need toincorporate multi-scale features arise. The resolution of input images can bereduced, however, with significant loss of critical information. Based on theoutlined issues, we introduce a novel research problem of training CNN modelsfor very large images, and present 'UltraMNIST dataset', a simple yetrepresentative benchmark dataset for this task. UltraMNIST has been designedusing the popular MNIST digits with additional levels of complexity added toreplicate well the challenges of real-world problems. We present two variantsof the problem: 'UltraMNIST classification' and 'Budget-aware UltraMNISTclassification'. The standard UltraMNIST classification benchmark is intendedto facilitate the development of novel CNN training methods that make theeffective use of the best available GPU resources. The budget-aware variant isintended to promote development of methods that work under constrained GPUmemory. For the development of competitive solutions, we present severalbaseline models for the standard benchmark and its budget-aware variant. Westudy the effect of reducing resolution on the performance and present resultsfor baseline models involving pretrained backbones from among the popularstate-of-the-art models. Finally, with the presented benchmark dataset and thebaselines, we hope to pave the ground for a new generation of CNN methodssuitable for handling large images in an efficient and resource-light manner.

UltraMNIST Classification: A Benchmark to Train CNNs for Very Large Images