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---
task_categories:
- image-classification
- unconditional-image-generation
pretty_name: Easy MNIST
size_categories:
- 10K<n<100K
---
# Easy MNIST
MNIST processed into three easy to use formats. Each .zip file contains a labels_and_paths.csv file and a data directory.
## mnist_png.zip
MNIST in the png format.
```
label path
0 5 data/0.png
1 0 data/1.png
2 4 data/2.png
3 1 data/3.png
4 9 data/4.png
... ... ...
69995 2 data/69995.png
69996 3 data/69996.png
69997 4 data/69997.png
69998 5 data/69998.png
69999 6 data/69999.png
```
## mnist_numpy.zip
MNIST in the npy format.
```
label path
0 5 data/0.npy
1 0 data/1.npy
2 4 data/2.npy
3 1 data/3.npy
4 9 data/4.npy
... ... ...
69995 2 data/69995.npy
69996 3 data/69996.npy
69997 4 data/69997.npy
69998 5 data/69998.npy
69999 6 data/69999.npy
```
## mnist_numpy_flat.zip
MNIST in the npy format, flattened to 784 dimensional vectors.
```
label path
0 5 data/0.npy
1 0 data/1.npy
2 4 data/2.npy
3 1 data/3.npy
4 9 data/4.npy
... ... ...
69995 2 data/69995.npy
69996 3 data/69996.npy
69997 4 data/69997.npy
69998 5 data/69998.npy
69999 6 data/69999.npy
```
## Acknowledgements
- Yann LeCun, Courant Institute, NYU
- Corinna Cortes, Google Labs, New York
- Christopher J.C. Burges, Microsoft Research, Redmond
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