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Vision and Learning Datasets

Object/Scene Recognition

  • Caltech-101: 101 categories, 30 training samples per category max.
  • Caltech-256: more of the same: 256 categories.
  • NORB (large set): NYU Object Recognition Benchmark: 5 categories, 10 instances per categories, texture-less objects under many poses and illuminations. Highly variable backgrounds, with small variation of scale and position (jittered-cluttered NORB).
  • NORB (small set): NYU rbject Recognition Benchmark: 5 categories, 10 instances per categories, texture-less objects under many poses. 6 illuminations, uniform backgrounds, no variation of scale and position (normalized-uniform NORB).
  • Pascal Visual Object Classes Challenge datasets.
  • LabelMe: segmented and labeled images from Antonio Torralba at MIT.
  • Oxford Buildings dataset: 5062 images of 11 different Oxford landmarks.
  • Image Parsing dataset from Song-Chun Zhu's Lotus Hill Institute in China.
  • Tiny Images: 1.5 million 32x32 images. The full set has 80 million images.
  • ETH-80: Objects from 8 categories, multiple instances, and multiple views on a blue background.
  • Photo-tourism patches: UBC/Microsoft aligned patches for invariant feature learning.
  • Oxford Flowers.
  • Priceton Event dataset: pictures of 8 sport activities with about 200 images per category.

Face, People, and Car Datasets for Detection and Recognition

Handwriting

  • MNIST: Handwritten digit dataset with 60,000 training samples and 10,000 test samples.

Video

Image retrieval

  • Accio!: content-based image retrieval dataset from WUSTL.

Pages with links to more datasets

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