metadata

language: en
license: mit
multilinguality: monolingual
task_categories:
  - image-classification
task_ids:
  - multi-class-image-classification
pretty_name: MNIST
dataset_info:
  features:
    - name: image
      dtype: image
    - name: label
      dtype: int64
  splits:
    - name: train
      num_bytes: 17223300
      num_examples: 60000
    - name: test
      num_bytes: 2875182
      num_examples: 10000
  download_size: 18157280
  dataset_size: 20098482
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: test
        path: data/test-*

🖼️ MNIST (Extracted from PyTorch Vision)

MNIST is a classic dataset of handwritten digits, widely used for image classification tasks in machine learning.

ℹ️ Dataset Details

📖 Dataset Description

The MNIST database of handwritten digits is a commonly used benchmark dataset in machine learning. It consists of 70,000 grayscale images of handwritten digits (0-9), each with a size of 28x28 pixels. The dataset is split into 60,000 training images and 10,000 testing images. MNIST is often used for evaluating and comparing different machine learning algorithms, particularly in the field of image recognition.

📂 Dataset Structure

Each data point is a pair:

image: A visual captured (stored as a PIL Image).
label: The corresponding label (an integer representing the class).

🚀 How to Use this Dataset

from datasets import load_dataset

dataset = load_dataset('MNIST')

🗄️ Source Data

Auto-generated from PyTorch Vision, please check the original MNIST for more info.

📜 License

mit