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--- |
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license: apache-2.0 |
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task_categories: |
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- image-classification |
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- unconditional-image-generation |
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language: |
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- en |
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size_categories: |
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- 10K<n<100K |
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--- |
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# MNIST 28×28 Grayscale Dataset |
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The original MNIST dataset with handwritten digits in 28×28 grayscale format, stored in efficient Parquet format for modern deep learning applications. |
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## Overview |
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This dataset contains the original MNIST handwritten digit dataset in its native format: |
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- **Format**: 28×28 grayscale images |
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- **Digit labels**: 0-9 (single-label classification) |
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- **Image format**: Grayscale PIL Images |
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- **Storage**: Parquet format for efficient loading |
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## Dataset Statistics |
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### Training Set (60,000 samples) |
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| Digit | Count | Digit | Count | |
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|-------|-------|-------|-------| |
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| 0 | 5,923 | 5 | 5,421 | |
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| 1 | 6,742 | 6 | 5,918 | |
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| 2 | 5,958 | 7 | 6,265 | |
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| 3 | 6,131 | 8 | 5,851 | |
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| 4 | 5,842 | 9 | 5,949 | |
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### Test Set (10,000 samples) |
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| Digit | Count | Digit | Count | |
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|-------|-------|-------|-------| |
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| 0 | 980 | 5 | 892 | |
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| 1 | 1,135 | 6 | 958 | |
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| 2 | 1,032 | 7 | 1,028 | |
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| 3 | 1,010 | 8 | 974 | |
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| 4 | 982 | 9 | 1,009 | |
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## Directory Structure |
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``` |
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mnist_28/ |
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├── README.md # This documentation |
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├── train-00000-of-00001.parquet # Training data (Parquet format) |
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└── test-00000-of-00001.parquet # Test data (Parquet format) |
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``` |
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## Key Features |
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- **Original Resolution**: Maintains original 28×28 pixel resolution |
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- **Grayscale Format**: Single-channel grayscale format as in original MNIST |
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- **PIL Integration**: Images loaded as PIL RGB objects ready for preprocessing |
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- **Standard Splits**: Maintains original MNIST train/test division |
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- **HuggingFace Compatible**: Full integration with datasets library |
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- **Efficient Loading**: Parquet format for fast columnar data access and compression |
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## Usage |
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### Loading with HuggingFace Datasets |
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```python |
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from datasets import load_dataset |
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# Load the dataset using the custom script |
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dataset = load_dataset("FrankCCCCC/mnist_28", trust_remote_code=True) |
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print(f"Train samples: {len(dataset['train'])}") |
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print(f"Test samples: {len(dataset['test'])}") |
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# Access a sample |
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sample = dataset['train'][0] |
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print(f"Image shape: {sample['image'].size}") # (28, 28) |
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print(f"Image mode: {sample['image'].mode}") # L (Grayscale) |
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print(f"Label: {sample['label']}") # Integer: 0-9 |
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``` |
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## Transformations Applied |
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The dataset preprocessing pipeline includes: |
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1. **Format Conversion**: IDX → Parquet |
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2. **Data Storage**: Parquet format for efficient storage and loading |
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3. **Data Type**: PIL Image objects for easy integration |
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4. **Format Preservation**: Maintains original 28×28 grayscale format |
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## Dataset Format |
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Each sample contains: |
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```python |
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{ |
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'image': PIL.Image, # 28×28 grayscale image |
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'label': int, # Digit class (0-9) |
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} |
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``` |
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## Technical Details |
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- **Original Source**: MNIST Database of Handwritten Digits |
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- **Format**: Parquet files for efficient columnar storage |
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- **Preprocessing**: Maintains original grayscale format and 28×28 resolution |
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- **Loading**: HuggingFace Datasets with custom GeneratorBasedBuilder |
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- **Compression**: Parquet format provides built-in compression and fast access |
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## Citation |
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```bibtex |
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@article{lecun1998mnist, |
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title={The MNIST database of handwritten digits}, |
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author={LeCun, Yann and Bottou, L{\'e}on and Bengio, Yoshua and Haffner, Patrick}, |
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year={1998}, |
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url={http://yann.lecun.com/exdb/mnist/} |
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} |
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@misc{mnist28, |
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title={MNIST 28×28 Grayscale Dataset}, |
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author={Original MNIST for Deep Learning}, |
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year={2024}, |
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note={Original MNIST dataset in efficient Parquet format} |
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} |
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``` |
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## License |
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This dataset follows the same license as the original MNIST dataset. The original MNIST database is available under the Creative Commons Attribution-Share Alike 3.0 license. |
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## Acknowledgments |
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- Based on the original MNIST dataset by Yann LeCun, Corinna Cortes, and Christopher J.C. Burges |
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- Preserved in original format for classical deep learning applications |
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- Compatible with HuggingFace Datasets ecosystem for seamless integration |
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- Optimized for CNN architectures and transfer learning applications |
