README

# Fine-Tuning ResNet50 for Alzheimer's MRI Classification

This repository contains a Jupyter Notebook for fine-tuning a ResNet50 model to classify Alzheimer's disease stages from MRI images. The notebook uses PyTorch and the dataset is loaded from the Hugging Face Datasets library.

## Table of Contents
- [Introduction](#introduction)
- [Dataset](#dataset)
- [Model Architecture](#model-architecture)
- [Setup](#setup)
- [Training](#training)
- [Evaluation](#evaluation)
- [Usage](#usage)
- [Results](#results)
- [Contributing](#contributing)
- [License](#license)

## Introduction
This notebook fine-tunes a pre-trained ResNet50 model to classify MRI images into one of four stages of Alzheimer's disease:
- Mild Demented
- Moderate Demented
- Non-Demented
- Very Mild Demented

## Dataset
The dataset used is [Falah/Alzheimer_MRI](https://huggingface.co/datasets/Falah/Alzheimer_MRI) from the Hugging Face Datasets library. It consists of MRI images categorized into the four stages of Alzheimer's disease.

## Model Architecture
The model architecture is based on ResNet50. The final fully connected layer is modified to output predictions for 4 classes.

## Setup
To run the notebook locally, follow these steps:

1. Clone the repository:
    ```bash
    git clone https://github.com/your_username/alzheimer_mri_classification.git
    cd alzheimer_mri_classification
    ```

2. Install the required dependencies:
    ```bash
    pip install -r requirements.txt
    ```

3. Open the notebook:
    ```bash
    jupyter notebook fine-tuning.ipynb
    ```

## Training
The notebook includes sections for:
- Loading and preprocessing the dataset
- Defining the model architecture
- Setting up the training loop with a learning rate scheduler and optimizer
- Training the model for a specified number of epochs
- Saving the trained model weights

### Example Training Code
```python
# Training loop example
for epoch in range(num_epochs):
    model.train()
    running_loss = 0.0
    for images, labels in train_loader:
        images, labels = images.to(device), labels.to(device)
        optimizer.zero_grad()
        outputs = model(images)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        running_loss += loss.item()
    print(f"Epoch [{epoch+1}/{num_epochs}], Loss: {running_loss/len(train_loader)}")