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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
-
-## 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.