The Meta-Dataset benchmark is a large few-shot learning benchmark and consists of multiple datasets of different data distributions. It does not restrict few-shot tasks to have fixed ways and shots, thus representing a more realistic scenario. It consists of 10 datasets from diverse domains:
ILSVRC-2012 (the ImageNet dataset, consisting of natural images with 1000 categories)
Omniglot (hand-written characters, 1623 classes)
Aircraft (dataset of aircraft images, 100 classes)
CUB-200-2011 (dataset of Birds, 200 classes)
Describable Textures (different kinds of texture images with 43 categories)
Quick Draw (black and white sketches of 345 different categories)
Fungi (a large dataset of mushrooms with 1500 categories)
VGG Flower (dataset of flower images with 102 categories),
Traffic Signs (German traffic sign images with 43 classes)
MSCOCO (images collected from Flickr, 80 classes).
All datasets except Traffic signs and MSCOCO have a training, validation and test split (proportioned roughly into 70%, 15%, 15%). The datasets Traffic Signs and MSCOCO are reserved for testing only.