README

README

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+# 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)}")