README.md

---
datasets:
- ylecun/mnist
language:
- en
metrics:
- accuracy
---
# 🧠 TinyCNN for MNIST (94K params)

This repository contains a lightweight Convolutional Neural Network (CNN) designed for the MNIST handwritten digit classification task. 
This model was train on shifted MNIST handwritten digit dataset
The model is optimized to be **small, fast, and easy to deploy**, suitable for both research and educational purposes.

---

## 📌 Model Summary

| Attribute | Value |
|---------|--------|
| Model Name | **TinyCNN** |
| Dataset | MNIST (28×28 grayscale digits) |
| Total Parameters | ~94,410 |
| Architecture | Conv-BN-ReLU ×3 → Global Avg Pool → FC |
| Input Shape | (1, 28, 28) |
| Output Classes | 10 |
| Framework | PyTorch |

---

## 🏗 Architecture Overview

**Input**: 1×28×28


**Conv Block 1:**  Conv(1→32, 3×3) → BatchNorm → ReLU → MaxPool(2×2)


**Conv Block 2:**  Conv(32→64, 3×3) → BatchNorm → ReLU → MaxPool(2×2)

**Conv Block 3:**  Conv(64→128, 3×3) → BatchNorm → ReLU → MaxPool(2×2)

**Global Average Pooling**

**Fully Connected Layer** → 10 output classes


This architecture emphasizes parameter efficiency while maintaining strong representation capability.

---

## ⚙️ Installation

```bash
pip install torch torchvision
```
## 🚀 Load Model From Hub
```
import torch

class TinyCNN(nn.Module):
    """
    Tiny CNN for MNIST using Global Avg Pooling.

    Trainable parameters: 94,410
    """
    
    def __init__(self, num_classes=10):
        super(TinyCNN, self).__init__()
        
        # First conv block
        self.conv1 = nn.Conv2d(1, 32, kernel_size=3, padding=1)
        self.bn1 = nn.BatchNorm2d(32)
        self.pool1 = nn.MaxPool2d(2, 2)
        
        # Second conv block
        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, padding=1)
        self.bn2 = nn.BatchNorm2d(64)
        self.pool2 = nn.MaxPool2d(2, 2)

        # Third conv block
        self.conv3 = nn.Conv2d(64, 128, kernel_size=3, padding=1)
        self.bn3 = nn.BatchNorm2d(128)
        self.pool3 = nn.MaxPool2d(2, 2)
        
        # Global average pooling
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        
        # Final FC (input = 128 channels after GAP)
        self.fc = nn.Linear(128, num_classes)

    def forward(self, x):
        x = self.pool1(F.relu(self.bn1(self.conv1(x))))
        x = self.pool2(F.relu(self.bn2(self.conv2(x))))
        x = self.pool3(F.relu(self.bn3(self.conv3(x))))
        x = self.avgpool(x)              # (batch, 64, 1, 1)
        x = x.view(x.size(0), -1)        # (batch, 64)
        x = self.fc(x)                   # (batch, num_classes)
        return x

model = TinyCNN(num_classes=10)

state_dict = torch.hub.load_state_dict_from_url(
    "https://huggingface.co/FinOS-Internship/ShiftedTinyCNN/TinyCNN_model_acc_98.97.pth"
)
model.load_state_dict(state_dict)
model.eval()
```

## 🖼 Example Inference
```
import torch
from torchvision import transforms
from PIL import Image

transform = transforms.Compose([
    transforms.Grayscale(),
    transforms.Resize((28, 28)),
    transforms.ToTensor()
])

img = Image.open("digit.png")
x = transform(img).unsqueeze(0)  # shape: (1, 1, 28, 28)

with torch.no_grad():
    logits = model(x)
    pred = logits.argmax(dim=1).item()

print("Predicted digit:", pred)
```