Datasets for Computer Vision (2)

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*My post explains MNIST, EMNIST, QMNIST, ETLCDB, Kuzushiji and Moving MNIST.

(1) Fashion-MNIST(2017):

• has the 70,000 fashion images each connected to a label from 10 classes with 10 classes: *Memos:
  • 60,000 for train and 10,000 for test.
  • Each image is 28x28 pixels.
• is FashionMNIST() in PyTorch.

(2) Caltech 101(2003):

• has the 8,677 object images each connected to a label from 101 categories(classes). *Each image is roughly 300x200 pixels.
• is Caltech101() in PyTorch.

(3) Caltech 256(2007):

• has the 30,607 object images connected to a label from 257 categories(classes). *Actually, it has 257 categories(classes) against the name Caltech 256.
• is Caltech256() in PyTorch.

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

• has the 202,599 celebrity face images each connected to 40 attributes: *Memos:
  • 162,770 for train, 19,867 for validation and 19,962 for test.
  • Directly downloading it from Google Drive is recommended because downloading it with Google Drive API from Google Drive is too crowded.
• is CelebA() in PyTorch.

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

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

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

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

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