README.md

---
language:
- en
license: mit
size_categories:
- 10k<n<100k
task_categories:
- image-classification
tags:
- mnist
- mytorch
- rjit
---

# 📊 MyTorch Refined MNIST Dataset

Curated by **Aryan Singh Chandel (Shiro)** at **Rustamji Institute of Technology (RJIT)**.

This dataset contains the refined version of the MNIST handwritten digit database, specifically pre-processed for compatibility with the **MyTorch** deep learning framework.

## 🏗️ Dataset Structure
The data is stored in a compressed NumPy format (`mnist_raw.npz`) containing:
- **X_train / y_train:** 60,000 samples for training.
- **X_test / y_test:** 10,000 samples for final validation.

## ⚡ Pre-processing Specification
Every image in this dataset has undergone the following transformation logic used in the MyTorch 98.59% accuracy run:
1. **Flattening:** $28 \times 28$ spatial grids converted to $\mathbb{R}^{784}$ feature vectors.
2. **Global Scaling:** Pixel values $P \in [0, 255]$ normalized to $P' \in [0, 1]$ via $P' = P / 255.0$.
3. **Refinement:** Standardized tensor shapes for high-speed matrix multiplication in NumPy-based linear layers.

## 🚀 Usage
To load this dataset into your own MyTorch project:
```python
import numpy as np
data = np.load('mnist_raw.npz')
X_train, y_train = data['X_train'], data['y_train']
```

🎓 Citation
If you use this refined dataset in your research, please attribute:
Chandel, A. S. (2026). MyTorch: Deep Learning from Scratch at RJIT.