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README.md

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

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