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README.md

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+---
+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.