MNIST - xyfJASON/image-datasets GitHub Wiki

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Brief introduction

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The MNIST database (Modified National Institute of Standards and Technology database) is a large collection of handwritten digits. It has a training set of 60,000 examples, and a test set of 10,000 examples. It is a subset of a larger NIST Special Database 3 (digits written by employees of the United States Census Bureau) and Special Database 1 (digits written by high school students) which contain monochrome images of handwritten digits. The digits have been size-normalized and centered in a fixed-size image. The original black and white (bilevel) images from NIST were size normalized to fit in a 20x20 pixel box while preserving their aspect ratio. The resulting images contain grey levels as a result of the anti-aliasing technique used by the normalization algorithm. the images were centered in a 28x28 image by computing the center of mass of the pixels, and translating the image so as to position this point at the center of the 28x28 field.

Statistics

Numbers: 70,000

Splits (train / test): 60,000 / 10,000

Resolution: 28×28

Labels：10 classes

Usage

File structure

Please organize the dataset in the following file structure:

root
└── MNIST
    └── raw
        ├── t10k-images-idx3-ubyte
        ├── t10k-images-idx3-ubyte.gz
        ├── t10k-labels-idx1-ubyte
        ├── t10k-labels-idx1-ubyte.gz
        ├── train-images-idx3-ubyte
        ├── train-images-idx3-ubyte.gz
        ├── train-labels-idx1-ubyte
        └── train-labels-idx1-ubyte.gz

Example

from torchvision.datasets import MNIST
train_set = MNIST(root='~/data/MNIST', train=True)
test_set = MNIST(root='~/data/MNIST', train=False)
print(len(train_set))  # 60000
print(len(test_set))   # 10000