Datasets:

tsilva
/

mnist

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	88	94
	00	95
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	33	98
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tsilva/mnist

Dataset Summary

This dataset is a Hugging Face packaged version of the classic MNIST handwritten digits benchmark. It contains grayscale 28x28 images of digits 0 through 9, split into the standard train and test partitions.

Each row includes:

image: a 28x28 grayscale digit image
label: the digit class from 0 to 9
source_index: the original position of the example inside the source MNIST split

Splits

train: 60,000 examples
test: 8,920 examples, balanced to 892 examples per class

Source

The underlying data comes from the original MNIST release maintained by Yann LeCun and collaborators and downloaded here through torchvision.datasets.MNIST.

MNIST homepage: http://yann.lecun.com/exdb/mnist/
TorchVision dataset docs: https://pytorch.org/vision/stable/generated/torchvision.datasets.MNIST.html

Intended Use

This dataset is suitable for standard handwritten digit classification baselines, representation learning experiments, and as a clean reference set for synthetic corruption studies.

Load Example

from datasets import load_dataset

ds = load_dataset("tsilva/mnist")
print(ds["train"][0])

Downloads last month: 21

Size of downloaded dataset files:

19.3 MB

Size of the auto-converted Parquet files:

19.3 MB

Number of rows:

68,920

image imagewidth (px) 28 28	label class label 10 classes	source_index int32 0 60k
	55	0
	00	1
	44	2
	11	3
	99	4
	22	5
	11	6
	33	7
	11	8
	44	9
	33	10
	55	11
	33	12
	66	13
	11	14
	77	15
	22	16
	88	17
	66	18
	99	19
	44	20
	00	21
	99	22
	11	23
	11	24
	22	25
	44	26
	33	27
	22	28
	77	29
	33	30
	88	31
	66	32
	99	33
	00	34
	55	35
	66	36
	00	37
	77	38
	66	39
	11	40
	88	41
	77	42
	99	43
	33	44
	99	45
	88	46
	55	47
	99	48
	33	49
	33	50
	00	51
	77	52
	44	53
	99	54
	88	55
	00	56
	99	57
	44	58
	11	59
	44	60
	44	61
	66	62
	00	63
	44	64
	55	65
	66	66
	11	67
	00	68
	00	69
	11	70
	77	71
	11	72
	66	73
	33	74
	00	75
	22	76
	11	77
	11	78
	77	79
	99	80
	00	81
	22	82
	66	83
	77	84
	88	85
	33	86
	99	87
	00	88
	44	89
	66	90
	77	91
	44	92
	66	93
	88	94
	00	95
	77	96
	88	97
	33	98
	11	99

image imagewidth (px) 28 28	label class label 10 classes	source_index int32 0 60k
	55	0
	00	1
	44	2
	11	3
	99	4
	22	5
	11	6
	33	7
	11	8
	44	9
	33	10
	55	11
	33	12
	66	13
	11	14
	77	15
	22	16
	88	17
	66	18
	99	19
	44	20
	00	21
	99	22
	11	23
	11	24
	22	25
	44	26
	33	27
	22	28
	77	29
	33	30
	88	31
	66	32
	99	33
	00	34
	55	35
	66	36
	00	37
	77	38
	66	39
	11	40
	88	41
	77	42
	99	43
	33	44
	99	45
	88	46
	55	47
	99	48
	33	49
	33	50
	00	51
	77	52
	44	53
	99	54
	88	55
	00	56
	99	57
	44	58
	11	59
	44	60
	44	61
	66	62
	00	63
	44	64
	55	65
	66	66
	11	67
	00	68
	00	69
	11	70
	77	71
	11	72
	66	73
	33	74
	00	75
	22	76
	11	77
	11	78
	77	79
	99	80
	00	81
	22	82
	66	83
	77	84
	88	85
	33	86
	99	87
	00	88
	44	89
	66	90
	77	91
	44	92
	66	93
	88	94
	00	95
	77	96
	88	97
	33	98
	11	99

image imagewidth (px) 28 28	label class label 10 classes	source_index int32 0 60k
	55	0
	00	1
	44	2
	11	3
	99	4
	22	5
	11	6
	33	7
	11	8
	44	9
	33	10
	55	11
	33	12
	66	13
	11	14
	77	15
	22	16
	88	17
	66	18
	99	19
	44	20
	00	21
	99	22
	11	23
	11	24
	22	25
	44	26
	33	27
	22	28
	77	29
	33	30
	88	31
	66	32
	99	33
	00	34
	55	35
	66	36
	00	37
	77	38
	66	39
	11	40
	88	41
	77	42
	99	43
	33	44
	99	45
	88	46
	55	47
	99	48
	33	49
	33	50
	00	51
	77	52
	44	53
	99	54
	88	55
	00	56
	99	57
	44	58
	11	59
	44	60
	44	61
	66	62
	00	63
	44	64
	55	65
	66	66
	11	67
	00	68
	00	69
	11	70
	77	71
	11	72
	66	73
	33	74
	00	75
	22	76
	11	77
	11	78
	77	79
	99	80
	00	81
	22	82
	66	83
	77	84
	88	85
	33	86
	99	87
	00	88
	44	89
	66	90
	77	91
	44	92
	66	93
	88	94
	00	95
	77	96
	88	97
	33	98
	11	99