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MNIST handwritten digit database, Yann LeCun and Corinna Cortes
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"THE MNIST DATABASE
of handwritten digits
Yann LeCun , Courant Institute, NYU
Corinna Cortes , Google Labs, New York
The MNIST database of handwritten digits, available from this page, has a
training set of 60,000 examples, and a test set of 10,000 examples. It
is a subset of a larger set available from NIST. The digits have
been size-normalized and centered in a fixed-size image.
It is a good database for people who want to try learning techniques
and pattern recognition methods on real-world data while spending minimal
efforts on preprocessing and formatting.
Four files are available on this site:
train-images-idx3-ubyte.gz :
training set images (9912422 bytes)
train-labels-idx1-ubyte.gz :
training set labels (28881 bytes)
t10k-images-idx3-ubyte.gz :
test set images (1648877 bytes)
t10k-labels-idx1-ubyte.gz :
test set labels (4542 bytes)
please note that your browser may uncompress these files without telling you .
If the files you downloaded have a larger size than the above, they have been
uncompressed by your browser. Simply rename them to remove the .gz extension.
Some people have asked me "my application can't open your image files".
These files are not in any standard image format. You have to write
your own (very simple) program to read them. The file format is described
at the bottom of this page.
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.
With some classification methods (particuarly template-based methods,
such as SVM and K-nearest neighbors), the error rate improves when the
digits are c"
....
read entire page
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