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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 - Dataset - Model Architecture - Setup - Training - Evaluation - Usage - Results - Contributing - 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/AlzheimerMRI 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: 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 alzheimermodelresnet50.pth. 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.