# Model Card: MNIST Classification

## Overview

- **Model Name:** MNIST Classification Model
- **Author:** [Charana H U]
- **Date:** [03/01/2023]

## Model Information

- **Architecture:** Feedforward Neural Network
- **Input Size:** 784
- **Hidden Size:** 100
- **Output Size (Number of Classes):** 10
- **Loss Function:** CrossEntropyLoss
- **Optimizer:** Adam
- **Learning Rate:** 0.001
- **Training Batch Size:** 100
- **Number of Epochs:** 2

## Data

- **Training Dataset:** MNIST
- **Testing Dataset:** MNIST

## Training

- **Training Device:** GPU (if available)


## Evaluation

- **Testing Accuracy:** [96%]

## Usage - Loading

- **Dependencies:** PyTorch, torchvision, matplotlib
- **How to Load the Model:** 
  ```python
  from torchvision import transforms
  from your_model_module import NeuralNet

  model = NeuralNet(input_size=784, hidden_size=100, num_classes=10)
  model.load_state_dict(torch.load("model/mnist_model.pt"))
  model.eval()
  ```

## Usage - Testing
  
- **How to Make Predictions:**

  Assuming you have an image tensor named 'image'
  ```python
  image = image.reshape(-1, 28*28)
  output = model(image)
  _, prediction = torch.max(output, 1)
  ```

## Limitations and Future Work

- **Limitations:** 
  - The model is relatively simple and may not perform well on more complex datasets.
  - Limited training data augmentation was applied.

- **Future Work:**
  - Experiment with different network architectures.
  - Explore advanced data augmentation techniques.
  - Fine-tune hyperparameters for better performance.


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