  High-resolution and variable-shape images have not yet been properly
addressed by the AI community. The approach of down-sampling data often used
with convolutional neural networks is sub-optimal for many tasks, and has too
many drawbacks to be considered a sustainable alternative. In sight of the
increasing importance of problems that can benefit from exploiting
high-resolution (HR) and variable-shape, and with the goal of promoting
research in that direction, we introduce a new family of datasets (MetH). The
four proposed problems include two image classification, one image regression
and one super resolution task. Each of these datasets contains thousands of art
pieces captured by HR and variable-shape images, labeled by experts at the
Metropolitan Museum of Art. We perform an analysis, which shows how the
proposed tasks go well beyond current public alternatives in both pixel size
and aspect ratio variance. At the same time, the performance obtained by
popular architectures on these tasks shows that there is ample room for
improvement. To wrap up the relevance of the contribution we review the fields,
both in AI and high-performance computing, that could benefit from the proposed
challenges.
