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ShiroOnigami23/
MyTorch-MNIST

Name: MyTorch-MNIST
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🧠 MyTorch Elite MNIST Model

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

This repository hosts the Elite MyTorch MNIST Model, which achieved a remarkable 98.59% accuracy on the MNIST handwritten digit classification task.

The model is built entirely from scratch using NumPy, replicating core deep learning functionalities. It incorporates advanced features such as AdamW optimizer, Batch Normalization, Dropout, and Label Smoothing to achieve state-of-the-art performance within a custom framework.

🚀 Model Architecture

This is a multi-layer perceptron (MLP) with a "Diamond Architecture": 784 (Input) -> 1024 -> 2048 (Expansion) -> 512 (Bottleneck) -> 10 (Output)

Key components:

Linear Layers with Kaiming Initialization
Batch Normalization
ReLU Activation Functions
Dropout for regularization

⚡ Training Details

Optimizer: AdamW with Weight Decay
Loss Function: Cross-Entropy Loss with Label Smoothing
Learning Rate Scheduler: StepLR
Epochs: 15
Batch Size: 256
Data Augmentation: Ultra-light Gaussian noise

📊 Performance

Validation Accuracy: 98.59%
Confusion Matrix: (See visuals/final_heatmap.png in the main GitHub repo)

📦 Files

best_model.pkl: The serialized Python pickle file containing the trained MyTorch model instance.

💡 Usage

To load and use this model in your MyTorch project:

python

import pickle
import numpy as np

# Assuming you have MyTorch installed or in your Python path
# from mytorch.nn.sequential import Sequential # (and other modules)

# Load the model
with open('best_model.pkl', 'rb') as f:
    loaded_model = pickle.load(f)

# Example inference (assuming X_test is your preprocessed test data)
# predictions = loaded_model(X_test)
# print(np.argmax(predictions, axis=1))

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

ShiroOnigami23/MyTorch-MNIST