Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification
This provides a standardized, expandable benchmark for researchers in few-shot learning, transfer learning, and meta-learning, though it is incremental as it builds on existing meta-dataset efforts.
The authors introduced Meta-Album, a meta-dataset for few-shot image classification comprising 40 open datasets from diverse domains, each with at least 20 classes and 40 examples per class, and demonstrated its utility on few-shot learning problems with the first 30 datasets.
We introduce Meta-Album, an image classification meta-dataset designed to facilitate few-shot learning, transfer learning, meta-learning, among other tasks. It includes 40 open datasets, each having at least 20 classes with 40 examples per class, with verified licences. They stem from diverse domains, such as ecology (fauna and flora), manufacturing (textures, vehicles), human actions, and optical character recognition, featuring various image scales (microscopic, human scales, remote sensing). All datasets are preprocessed, annotated, and formatted uniformly, and come in 3 versions (Micro $\subset$ Mini $\subset$ Extended) to match users' computational resources. We showcase the utility of the first 30 datasets on few-shot learning problems. The other 10 will be released shortly after. Meta-Album is already more diverse and larger (in number of datasets) than similar efforts, and we are committed to keep enlarging it via a series of competitions. As competitions terminate, their test data are released, thus creating a rolling benchmark, available through OpenML.org. Our website https://meta-album.github.io/ contains the source code of challenge winning methods, baseline methods, data loaders, and instructions for contributing either new datasets or algorithms to our expandable meta-dataset.