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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
+
+## Evaluation
+The notebook includes a section for evaluating the trained model on the validation set. It calculates and prints the validation loss and accuracy.
+
+## Usage
+Once trained, the model can be saved and used for inference on new MRI images. The trained model weights are saved as alzheimer_model_resnet50.pth.
+
+##  Load the model architecture and weights
+     ```python
+    model = models.resnet50(weights=None)
+    model.fc = nn.Linear(model.fc.in_features, 4)
+    model.load_state_dict(torch.load("alzheimer_model_resnet50.pth", map_location=torch.device('cpu')))
+    model.eval()
+    ```
+## Results
+The model achieved an accuracy of 95.9375% on the validation set.
+## Contributing
+Contributions are welcome! If you have any suggestions, bug reports, or feature requests, please open an issue or submit a pull request.