Upload checkpoint and readme file

README.md

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+# 🧠 TinyCNN for MNIST (94K params)
+
+This repository contains a lightweight Convolutional Neural Network (CNN) designed for the MNIST handwritten digit classification task.  
+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
+from model import TinyCNN  # Ensure this file is included in your repo
+
+model = TinyCNN(num_classes=10)
+
+state_dict = torch.hub.load_state_dict_from_url(
+    "https://huggingface.co/<your-username>/<your-model-repo>/resolve/main/tinycnn_mnist.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)
+```

TinyCNN_model_acc_98.97.pth

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+size 388607