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Home Backend Development Python Tutorial Datasets for Computer Vision (1)

Datasets for Computer Vision (1)

Dec 17, 2024 pm 02:16 PM

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(1) MNIST(Modified National Institute of Standards and Technology)(1998):

  • has the 70,000 handwritten digits[0~9] by 28x28 pixels each. *60,000 for train and 10,000 for test.
  • is MNIST() in PyTorch.

Datasets for Computer Vision (1)

(2) EMNIST(Extended MNIST)(2017):

  • has the handwritten characters(digits[0~9] and alphabet letters[A~Z][a~z]) by 28x28 pixels each, splitted into 6 datasets(ByClass, ByMerge, Balanced, Letters, Digits and MNIST): *Memos:
    • ByClass has 814,255 characters(digits[0~9] and alphabet letters[A~Z][a~z]). *697,932 for train and 116,323 for test.
    • ByMerge has 814,255 characters(digits[0~9] and alphabet letters[A~Z][a, b, d~h, n, q, r, t]). *697,932 for train and 116,323 for test.
    • Balanced has 131,600 characters(digits[0~9] and alphabet letters[A~Z][a, b, d~h, n, q, r, t]). *112,800 for train and 18,800 for test.
    • Letters has 145,600 alphabet letters[a~z]. *124,800 for train and 20,800 for test.
    • Digits has 280,000 digits[0~9]. *240,000 for train and 40,000 for test.
    • MNIST has 70,000 digits[0~9]. *60,000 for train and 10,000 for test.
  • is EMNIST() in PyTorch.

Datasets for Computer Vision (1)

(3) QMNIST(2019):

  • has 120,000 handwritten digits[0~9] by 28x28 pixels each. *60,000 for train and 60,000 for test.
  • is an extended MNIST. *I don't know what Q of QMNIST means.
  • is QMNIST() in PyTorch.

Datasets for Computer Vision (1)

(4) ETLCDB(Extract-Transform-Load Character Database)(2011):

  • has the handwritten or machine-printed numerals, symbols, alphabet letters and Japanese characters split into 9 datasets(ETL-1, ETL-2, ETL-3 , ETL-4, ETL-5, ETL-6, ETL-7, ETL-8 and ETL-9) : : : : : : : : : : : : : : : : : : : : : : *Memos:
    • ETL1 has 141,319 characters (digits[0~9], alphabet letters[A~Z], symbols[-*/=()?,?'] and Katakana[ア~ン]).
    • ETL2 has 52,796 characters(digits[0~9], alphabet letters[A~Z], symbols, Katakana letters[ア~ン], Hiragana letters[あ~ん] and Kanji letters).
    • ETL3 has 9,600 characters(digits[0~9], alphabet letters[A~Z] and symbols[¥ -*/=()?,_?]).
    • ETL4 has 6,120 letters[あ~ん].
    • ETL5 has 10,608 Katakana letters[ア~ン].
    • ETL6 has 52,796 characters (digits[0~9], alphabet letters[A~Z][a~z], symbols and Katakana letters[ア~ン]).
    • ETL7(ETL7L and ETL7S) has 16,800 characters
    • ETL8(ETL8G and ETL8B2) has 152,960 characters
    • ETL9(ETL9G and ETL9B)
    • has 607,200 characters
    • It's not in PyTorch so we need to download it from etlcdb.

(5) Kuzushiji(2018):Datasets for Computer Vision (1)

The cursive style of Japanese characters is split into 3 datasets(

Kuzushiji-MNIST
    ,
  • Kuzushiji-49 and Kuzushiji-Kanji): *Memos: Kuzushiji-MNIST
      has 28x28 pixels resolution
    • Kuzushiji-49 has 28x28 pixels each.
    • Kuzushiji-49
    • Kuzushiji-Kanji
    • has the imbalanced 140,424 Kanji characters by 64x64 pixels each.
    • KMNIST() is in PyTorch but it only has
    Kuzushiji-MNIST
  • ??>
  • (6) Moving MNIST(2015):
  • has 10,000 videos by 64x64 pixels each. *Each video has 20 frames with 2 moving digits.

MovingMNIST() is in PyTorch.Datasets for Computer Vision (1)

    Datasets for Computer Vision (1)

    Datasets for Computer Vision (1)

    Datasets for Computer Vision (1)

    Datasets for Computer Vision (1)

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