Datasets:
Tasks:
Image Classification
Modalities:
Image
Formats:
parquet
Sub-tasks:
multi-class-image-classification
Languages:
English
Size:
10K - 100K
License:
File size: 1,677 Bytes
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---
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 |