README.md

# MNIST Digit Classifier

**Model type**: Convolutional Neural Network (CNN)  
**Model Architecture**: 3 Convolutional Layers, 1 Adaptive Pooling Layer, 1 Fully Connected Layer  
**Framework**: PyTorch  
**Task**: Image Classification (Digits 0-9)

## Model Description

This model is a Convolutional Neural Network (CNN) trained on the MNIST dataset, which consists of handwritten digits (0-9). It is designed to classify images of handwritten digits, making it suitable for applications that require digit recognition, such as form scanning, document analysis, and real-time digit detection.

### Model Architecture:
- **Convolutional Layers**: 3 convolutional layers with ReLU activations.
- **Adaptive Pooling**: Adaptive Average Pooling is used to ensure dynamic input handling.
- **Fully Connected Layer**: The output from the convolutional layers is flattened and fed into a fully connected layer that outputs 10 logits corresponding to the digits 0-9.

## Training Data

The model was trained on the [MNIST dataset](http://yann.lecun.com/exdb/mnist/), which contains 60,000 training examples and 10,000 test examples of 28x28 grayscale images of handwritten digits.

### Data Preprocessing:
- **Data Augmentation**: Random rotations (up to 10 degrees) and translations (up to 10% shift) were applied to the training data to improve generalization and robustness.
- **Normalization**: Each pixel was normalized to the range [-1, 1] by using the following normalization parameters:
  - Mean: 0.5
  - Standard Deviation: 0.5

## Intended Use

This model is suitable for:
- Recognizing handwritten digits in real-world applications such as scanned documents, forms, or digit-based input systems.
- Educational purposes to demonstrate digit classification using neural networks.

**How to use**:  
The model can be loaded using PyTorch, and an image can be classified by following this code snippet:

\`\`\`python
import torch
from torchvision import transforms
from PIL import Image

# Load the model
model = YourModelClass()
model.load_state_dict(torch.load('mnist_classifier.pth'))
model.eval()

# Preprocess image
transform = transforms.Compose([
    transforms.Resize((28, 28)),
    transforms.ToTensor(),
    transforms.Normalize((0.5,), (0.5,))
])

img = Image.open('path_to_image').convert('L')
img_tensor = transform(img).unsqueeze(0)

# Predict
with torch.no_grad():
    output = model(img_tensor)
    predicted_label = torch.argmax(output, dim=1).item()

print(f"Predicted Label: {predicted_label}")
\`\`\`

## Evaluation Results

### Metrics:
The model achieved the following performance metrics on the MNIST test dataset:
- **Accuracy**: ~98%
- **Loss**: Cross-entropy loss during training converged to a low value (~0.15 after 10 epochs).

### Noisy Image Performance:
The model was also tested on noisy digit images and successfully classified digits with preprocessing applied (e.g., Gaussian blur and thresholding).

## Limitations

- **Noisy Inputs**: The model might still struggle with images that are heavily noisy or distorted, though preprocessing techniques like Gaussian blur and normalization help mitigate these issues.
- **Generalization**: The model is designed specifically for MNIST-like digits and might not generalize well to digit styles that are too different from the MNIST dataset (e.g., digits from different cultures or handwriting styles).

## Training Details

### Hyperparameters:
- **Optimizer**: Adam
  - Learning Rate: 0.001
- **Loss Function**: Cross-entropy Loss
- **Batch Size**: 32
- **Epochs**: 10
- **Data Augmentation**: Random rotations and translations during training

## Ethical Considerations

While this model does not have significant ethical concerns, users should be aware that it is trained on a specific dataset (MNIST) that consists only of simple, grayscale digits. It may not perform well on digits outside of this domain (e.g., digits from other scripts or more complex scenarios).

## Model Card Contact

If you have any questions, feedback, or inquiries about this model, feel free to reach out to the author via [[email protected]].