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Super Kai (Kazuya Ito)
Super Kai (Kazuya Ito)

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Datasets for Computer Vision (2)

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*Memos:

(1) Fashion-MNIST(2017):

  • has the 70,000 fashion images each connected to the label from 10 classes: *Memos:
    • 60,000 for train and 10,000 for test.
    • Each image has 28x28 pixels.
  • is FashionMNIST() in PyTorch. *My post explains FashionMNIST().
  • is used for Image Classification. Image description

(2) Caltech 101(2003):

  • has the 8,677 object images each connected to the label from 101 categories(classes). *Each image has roughly 300x200 pixels.
  • is used for Image Classification.
  • is Caltech101() in PyTorch. *My post explains Caltech101().

Image description

(3) Caltech 256(2007):

  • has the 30,607 object images connected to the label from 257 categories(classes). *Actually, it has 257 categories(classes) against the name Caltech 256.
  • is used for Image Classification.
  • is Caltech256() in PyTorch. *My post explains Caltech256().

Image description

Image description

(4) CelebA(Large-scale CelebFaces Attributes)(2015):

  • has the 202,599 celebrity face images each connected to the label from 8192 unique identities(classes) and each connected to 40 attributes: *Memos:
    • 162,770 for train, 19,867 for validation and 19,962 for test.
    • Each image has 5 landmarks.
    • Directly downloading it from Google Drive is recommended because downloading it with Google Drive API from Google Drive is too crowded.
  • is used for Keypoint Detection and Fine-Grained Image Classification.
  • is CelebA() in PyTorch. *My post explains CelebA().

Image description

(5) CIFAR-10(Canadian Institute For Advanced Research-10)(2009):

  • has the 60,000 vehicle and animal images each connected to the label from 10 classes: *Memos:
    • 50,000 for train and 10,000 for test.
    • Each image has 32x32 pixels.
  • is used for Image Classification.
  • is CIFAR10() in PyTorch. *My post explains CIFAR10().

Image description

(6) CIFAR-100(Canadian Institute For Advanced Research-100)(2009):

  • has the 60,000 object images each connected to the label from 100 classes: *Memos:
    • 50,000 for train and 10,000 for test.
    • Each image has 32x32 pixels.
  • is used for Image Classification.
  • is CIFAR100() in PyTorch. *My post explains CIFAR100().

Image description

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