---
license: cc0-1.0
task_categories:
- image-classification
- clustering
tags:
- mnist
- digits
- computer-vision
- machine-learning
- matlab
dataset_info:
  features:
    - name: data
      dtype: uint8
      shape: [784]
    - name: labels
      dtype: uint8
  splits:
    - name: train
      num_bytes: 3170000000
      num_examples: 8100000
---

# MNIST8M Dataset (.mat format)

## Dataset Description

This repository contains the MNIST8M dataset converted to MATLAB `.h5` format for convenient use in MATLAB environments. The original data is sourced from the LIBSVM datasets page.

### Dataset Summary

- **Original Source**: [LIBSVM Multiclass Datasets - MNIST8M](https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multiclass.html#mnist8m)
- **Format Conversion**: Converted from original LibSVM format to MATLAB `.h5` format
- **Purpose**: Facilitate clustering and machine learning experiments in MATLAB
- **Files**: `MNIST8M_data.h5`, `labels.mat`

### Data Specifications

- **Samples**: 8,100,000 (8.1 million)
- **Features**: 784 (28×28 pixel images)
- **Data Type**: `uint8`
- **Value Range**: [0, 255]
- **Labels**: 10 classes (digits 0-9)
- **Label Type**: `uint8`
- **Label Range**: [0, 9]

### Storage Format

- `MNIST8M_data`: uint8 matrix of size 8,100,000 × 784
- `labels`: uint8 vector of size 8,100,000 × 1

### Usage Warning

⚠️ **Memory Considerations**: Loading the entire dataset directly into memory may cause out-of-memory errors on systems with insufficient RAM. The uncompressed data requires approximately 6GB of memory (8.1M × 784 × 1 byte).

### Recommended Usage

For systems with limited memory, consider:
- Loading data in batches
- Using memory-mapped files
- Working with data subsets
- Converting to single precision when possible

### Source Attribution

Original dataset courtesy of:
- [LIBSVM Datasets](https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multiclass.html#mnist8m)
- [infinite MNIST](https://leon.bottou.org/projects/infimnist)

### MATLAB Loading Example

```
dataset_name = 'MNIST8M';

% Data path
data_file = 'MNIST8M_data.h5'; % Your directory
data = h5read(data_file, '/MNIST8M');
data = double(data); % May cause out-of-memory
data_info = h5info(data_file);
data_size = data_info.Datasets.Dataspace.Size;
n_points = data_size(1);   % Total number of points
n_features = data_size(2);       % Number of dimensions
```

```
>> h5disp('MNIST8M_data.h5');
HDF5 MNIST8M_data.h5 
Group '/' 
    Dataset 'MNIST8M' 
        Size:  8100000x784
        MaxSize:  8100000x784
        Datatype:   H5T_STD_U8LE (uint8)
        ChunkSize:  100000x784
        Filters:  none
        FillValue:  0
```