by Ciro Santilli (@cirosantilli, 35)
70,000 28x28 grayscale (1 byte per pixel) images of hand-written digits 0-9, i.e. 10 categories. 60k are considered training data, 10k are considered for test data.
Playing with it is the de-facto computer vision hello world.
It was on this dataset that Yann LeCun made great progress with the LeNet model. Running LeNet on MNIST has to be the most classic computer vision thing ever. See e.g. activatedgeek/LeNet-5 for a minimal and modern PyTorch educational implementation.
But it is important to note that as of the 2010's, the benchmark had become too easy for many applications. It is perhaps fair to say that the next big dataset revolution of the same importance was with ImageNet.
The dataset could be downloaded from yann.lecun.com/exdb/mnist/ but as of March 2025 it was down and seems to have broken from time to time randomly, so Wayback Machine to the rescue:
wget \
 https://web.archive.org/web/20120828222752/http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz \
 https://web.archive.org/web/20120828182504/http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz \
 https://web.archive.org/web/20240323235739/http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz \
 https://web.archive.org/web/20240328174015/http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz
but doing so is kind of pointless as both files use some crazy single-file custom binary format to store all images and labels. OMG!
Figure 1.
MNIST image 1 of a '0'
.
Figure 2.
MNIST image 21 of a '0'
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Figure 3.
MNIST image 3 of a '1'
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Same style as MNIST, but with clothes. Designed to be much harder, and more representative of modern applications, while still retaining the low resolution of MNIST for simplicity of training.

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