Image Classification with CNN Feature Extraction and Logistic Regression

This project demonstrates an approach to image classification using a pre-trained Convolutional Neural Network (CNN) for feature extraction, followed by a Logistic Regression model for final classification. This method leverages the power of transfer learning to achieve good performance with potentially less training data compared to training a CNN from scratch.

The core idea is to utilize a pre-trained CNN (specifically VGG16 in this case) as a fixed feature extractor. The features generated by the CNN are then fed into a simpler, trainable model like Logistic Regression for the classification task.

Table of Contents

• Project Overview
• Features
• Technologies Used
• Getting Started
  • Prerequisites
  • Installation
  • Usage
• Project Structure
• Results
• Contributing
• License
• Contact

Project Overview

The project involves the following key steps:

1. Data Preparation: Loading and preprocessing image data.
2. Transfer Learning with VGG16: Utilizing a pre-trained VGG16 model (without its top classification layer) to extract features from the images.
3. Feature Extraction: Passing the image data through the VGG16 base model to obtain a set of high-level features for each image.
4. Logistic Regression Classification: Training a Logistic Regression model on the extracted features to perform the final classification.
5. Model Evaluation: Assessing the performance of the combined model using various metrics.

Features

• Transfer Learning: Efficiently leverages knowledge from a large pre-trained model (VGG16).
• Feature Extraction: Uses the CNN as a powerful feature extractor, reducing the need for extensive manual feature engineering.
• Logistic Regression: Employs a simple yet effective classifier for the final prediction stage.
• Clear Workflow: The Jupyter notebook provides a step-by-step demonstration of the entire process.

Technologies Used

• Python 3.x
• TensorFlow / Keras
• Scikit-learn
• NumPy
• Matplotlib
• Seaborn
• Jupyter Notebook

Getting Started

To get a local copy up and running, follow these simple steps.

Prerequisites

Ensure you have Python 3.x installed. You can download it from python.org.

Installation

1. Clone the repository:

   git clone [https://github.com/your-username/your-repository-name.git](https://github.com/your-username/your-repository-name.git)
   cd your-repository-name

2. Install the required packages:

   pip install -r requirements.txt

   (Note: You might need to create a requirements.txt file based on the imports in CNN_Detection.ipynb) A basic requirements.txt might look like this:

   tensorflow
   keras
   scikit-learn
   numpy
   matplotlib
   seaborn
   jupyter
   

Usage

1. Open the Jupyter notebook:

   jupyter notebook CNN_Detection.ipynb

2. Run all cells in the notebook to execute the entire workflow, from data loading to model evaluation.

Project Structure

├── CNN_Detection.ipynb ├── data/ │ └── (Your image dataset goes here, e.g., train/, test/, validation/) ├── README.md ├── requirements.txt └── (any other supporting files or directories)

• CNN_Detection.ipynb: The main Jupyter notebook containing all the code for data loading, model training, and evaluation.
• data/: This directory should contain your image dataset, organized into appropriate subdirectories (e.g., train, test, validation) with subfolders for each class.