---
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: 17223300.0
    num_examples: 60000
  - name: test
    num_bytes: 2875182.0
    num_examples: 10000
  download_size: 18157280
  dataset_size: 20098482.0
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

```python
from datasets import load_dataset

dataset = load_dataset('MNIST')
```

## 🗄️ Source Data

Auto-generated from PyTorch Vision, please check the original [MNIST](http://yann.lecun.com/exdb/mnist/) for more info.

## 📜 License

mit