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Datasets:
MarveenLee
/
MNIST8M

Tasks:
Image Classification
Tags:
mnist
digits
computer-vision
machine-learning
matlab
License:
Dataset card Files
xet
Community
Dataset Viewer
The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
text: string
data: null
labels: null
to
{'data': Value('uint8'), 'labels': Value('uint8')}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
                  return get_rows(
                         ^^^^^^^^^
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                         ^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/src/worker/utils.py", line 77, in get_rows
                  rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2431, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 1952, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 1984, in _iter_arrow
                  pa_table = cast_table_to_features(pa_table, self.features)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2192, in cast_table_to_features
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              text: string
              data: null
              labels: null
              to
              {'data': Value('uint8'), 'labels': Value('uint8')}
              because column names don't match

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YAML Metadata Warning: The task_categories "clustering" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other

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
  • 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:

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
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