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.

Source: Optimized Generic Feature Learning for Few-shot Classification across Domains


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