Datasets:

tsilva
/

mnist

dataset_info:
  features:
    - name: image
      dtype: image
    - name: label
      dtype:
        class_label:
          names:
            '0': '0'
            '1': '1'
            '2': '2'
            '3': '3'
            '4': '4'
            '5': '5'
            '6': '6'
            '7': '7'
            '8': '8'
            '9': '9'
    - name: source_index
      dtype: int32
  splits:
    - name: train
      num_bytes: 17155689
      num_examples: 60000
    - name: test
      num_bytes: 2555832
      num_examples: 8920
  download_size: 19348835
  dataset_size: 19711521
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: test
        path: data/test-*

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])