Cat-And-Dog-Image-Classification-Using-Support-Vector-Machine

Table of Contents

1. Introduction
2. Installation
3. Usage
4. Project Structure
5. Results
6. Contributing
7. License
8. Author

Introduction

Image classification is a crucial task in computer vision. In this project, we use a Support Vector Machine (SVM) to classify images of cats and dogs. The dataset used in this project consists of labeled images of cats and dogs.

Installation

To run this project, you need to have Python and Jupyter Notebook installed on your machine. You can install the required dependencies using the following command:

pip install -r requirements.txt

Usage

1. Clone the repository:

   git clone https://github.com/yourusername/cat-dog-classification-svm.git
   cd cat-dog-classification-svm
   

2. Create a virtual environment:

   python -m venv venv
   

3. Navigate to the project directory:

   cd cat-dog-classification-svm
   

4. Install the dependencies:

   pip install -r requirements.txt
   

5. Open the Jupyter Notebook: jupyter notebook

6. Open the Cat And Dog Image Classification Using SVM.ipynb notebook and run the cells to train and evaluate the SVM model.

Project Structure

1. Cat And Dog Image Classification Using SVM.ipynb: The main Jupyter Notebook containing the code for the project.
2. requirements.txt: A file listing the required Python packages.
3. data/: A directory to store the dataset (not included in the repository).

Results

The results of the model training and evaluation, including accuracy and confusion matrix, are displayed in the Jupyter Notebook. You can visualize the performance of the model on the test dataset.

Contributing

Contributions are welcome! Please open an issue or submit a pull request for any improvements or bug fixes.

License

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

Author

For any questions or suggestions, please contact:

• Harsh Singh: harshjuly12@gmail.com
• GitHub: harshjuly12