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Cats and Dogs Classification App

This project is a simple web application that uses a VGG16 feature extraction model combined with a logistic regression classifier to classify images as either cats or dogs. The app is built using Streamlit, a popular Python library for creating web applications with minimal effort.

Table of Contents

• Getting Started
  • Prerequisites
  • Installation
• Usage
• Project Structure
• Contributing
• License

Getting Started

Prerequisites

Before you begin, ensure you have met the following requirements:

• Python 3.6+
• You should have a basic understanding of machine learning concepts, Python, and Streamlit.

Installation

1. Clone the repository:

   git clone https://github.com/your-username/cats-dogs-classification-app.git

2. Change to the project directory:

   cd cats-dogs-classification-app

3. Install the required Python packages:

   pip install -r requirements.txt

Usage

1. Start the Streamlit app:

   streamlit run app.py

2. Open a web browser and navigate to the provided URL (usually http://localhost:8501).

3. Use the app to upload an image and click the "Classify" button to see if it's classified as a cat or a dog.

Project Structure

The project is organized as follows:

• app.py: The main Streamlit application file.
• vgg16_feature_extraction.py: Python script for feature extraction using a pre-trained VGG16 model.
• logistic_regression_classifier.py: Python script for training and using a logistic regression classifier.
• models/: Directory containing pre-trained VGG16 model weights.
• data/: Directory where training data and model checkpoints can be stored.

Contributing

Contributions are welcome! If you'd like to contribute to this project, please follow these guidelines:

1. Fork the repository.
2. Create a new branch for your feature or bug fix: git checkout -b feature-name.
3. Make your changes and commit them: git commit -m 'Add some feature'.
4. Push to the branch: git push origin feature-name.
5. Create a pull request with a clear title and description.

License

This project is licensed under the MIT License - see the LICENSE file for details.

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