Some tasks are inferred based on the benchmarks list.
The benchmarks section lists all benchmarks using a given dataset or any of
its variants. We use variants to distinguish between results evaluated on
slightly different versions of the same dataset. For example, ImageNet 32⨉32
and ImageNet 64⨉64 are variants of the ImageNet dataset.
ImageNet-O consists of images from classes that are not found in the ImageNet-1k dataset. It is used to test the robustness of vision models to out-of-distribution samples. It's reported using the AUPR metric.