# Project Overview

This repository contains the code and dataset for a classification task using the EfficientNet-V2BO model. The project is structured in multiple code files that perform different operations on the dataset. Below are the details of the code and the dataset used:

## 1. Classification File
The code in the classification file allows the raw dataset to be reclassified using the **EfficientNet-V2BO** model. This code divides the dataset into **5 separate classes**. The model is designed to handle classification tasks efficiently and produce reliable results.

## 2. Code 1 - Self Model Code
Code 1 represents the **self model code**, which is used to process the entire dataset through a **single model**. This code applies the classification model to the dataset in one pass and provides predictions based on the entire data.

## 3. Code 2 - Cross-Replication Model Code
Code 2 contains the **Cross-Replication model codes**. This code divides the entire dataset into **5 different replications**, each serving different operations and transformations. It allows for better handling of the dataset and ensures robust model training through cross-replication techniques.

## 4. Dataset Info
The dataset used in this project consists of labeled data for classification into 5 distinct classes. The dataset is pre-processed and ready for training and testing with the provided models. It includes images and their corresponding labels, making it suitable for deep learning classification tasks.

features:

name: image
dtype: image
name: label
dtype:
class_label:
names:
'0': Mild_Demented
'1': Moderate_Demented
'2': Non_Demented
'3': Very_Mild_Demented
splits:
name: train
num_bytes: 22560791.2
num_examples: 5120
name: test num_bytes: 5637447.08 num_examples: 1280 download_size: 28289848 dataset_size: 28198238.28 license: apache-2.0 task_categories:
image-classification
language:
en
tags:
medical
pretty_name: Alzheimer_MRI Disease Classification Dataset
size_categories:
- 1K<n<10K
Alzheimer_MRI Disease Classification Dataset
The Falah/Alzheimer_MRI Disease Classification dataset is a valuable resource for researchers and health medicine applications. This dataset focuses on the classification of Alzheimer's disease based on MRI scans. The dataset consists of brain MRI images labeled into four categories:

'0': Mild_Demented
'1': Moderate_Demented
'2': Non_Demented
'3': Very_Mild_Demented
Dataset Information
Train split:

Name: train
Number of bytes: 22,560,791.2
Number of examples: 5,120
Test split:

Name: test
Number of bytes: 5,637,447.08
Number of examples: 1,280
Download size: 28,289,848 bytes

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Feel free to explore the individual code files and datasets for further understanding of the implementation and structure of this project.