🐱🐶 Cat and Dog Image Classification Using CNN

This repository provides a simple Flask-based web application to classify images of cats 🐱 and dogs 🐶 using a Convolutional Neural Network (CNN). The application utilizes a pre-trained model ( trained by train_model.py ) stored in the model folder delivers an accuracy of 99.99% 🎯, ensuring reliable predictions for uploaded images.

✨ Features

• 🔥 High Accuracy: Achieves an impressive accuracy of 99.99% on the dataset.
• 🌟 User-Friendly Interface: Easily upload an image and get instant results.
• 🧠 Pre-Trained Model: Leverages a powerful CNN for efficient binary classification ( trained by train_model.py ).
• ⚡ Lightweight Flask Application: A simple yet powerful backend for seamless operation.
• 📂 Dataset Management: Includes a script for automated dataset download and organization.
• 🚀 Future-Ready: Designed for scalability with plans for enhancements like multi-class classification and real-time predictions.

🚀 Getting Started

✅ Prerequisites

• 🐍 Python 3.7 or higher
• 📦 pip for managing Python packages

⚙️ Installation

1. Clone the Repository

    git clone https://github.com/MohammadAshmir786/Cat_vs_Dog_Image_Classification_App.git
    cd Cat_vs_Dog_Image_Classification_App.git

2. Install the required libraries:

    pip install -r requirements.txt
   

3. Install kagglehub to download the dataset:

    pip install kagglehub

🖼️ Usage

Step 1: Download the Dataset

📥 Download the dataset using the following command:

 python download_dataset.py

This script will download the dataset and print the path to the downloaded folder. Create a folder named data in the repository and move the dataset into it.

Step 2: Run the Application

1. ✅ Ensure the pre-trained model file is present in the model folder.

2. ▶️ Start the Flask application:

   python app.py

3. 🌐 Open your web browser and navigate to http://localhost:5000/.

4. 📤 Upload an image of a cat 🐱 or dog 🐶 to the application.

5. 🎉 View the predicted class along with the probability score.

🤝 Contribution

Contributions to this project are welcome! Follow the steps below to contribute:

1. Fork the Repository:

   git clone https://github.com/your-username/cat-dog-classification.git

2. Create a new branch for your feature or bug fix:

   git checkout -b feature-name

3. Commit your changes:

   git commit -m "Description of your changes"

4. Push the branch to your fork:

   git push origin feature-name

5. Submit a Pull Request on the original repository.

🛠️ Troubleshooting

Common Issues and Solutions

1. ❌ Flask Application Not Starting

   • Ensure all dependencies are installed using requirements.txt.
   • Verify Python version compatibility (Python 3.7 or higher).

2. 📂 Dataset Not Found

   • Run the download_dataset.py script to download the dataset.
   • Place the dataset in the data folder.

3. ⚠️ Model File Missing

   • Ensure the pre-trained model file is available in the model folder.

4. 🔧 KaggleHub Installation Issue

   • Reinstall KaggleHub using:

     pip install kagglehub

🚀 Future Enhancements

1. 🔍 Enhanced Model Performance:

   • Further train the model with augmented datasets.
   • Explore advanced architectures like ResNet, VGG, or EfficientNet.

2. 🎨 UI Improvements:

   • Add drag-and-drop functionality for uploading images.
   • Improve visualization and user interaction.

3. ☁️ Deployment:

   • Deploy the application on platforms like AWS, Google Cloud, or Heroku.

4. 🐾 Multi-Class Classification:

   • Expand the model to classify additional animal species beyond cats 🐱 and dogs 🐶.

5. 📹 Real-Time Classification:

   • Integrate live webcam support for real-time image predictions.

🙌 Acknowledgments

• 💻 Flask : For providing a lightweight and efficient web framework.
• 📊 kagglehub : For facilitating easy access to datasets.

📜 License

This project is licensed under the MIT License ↗️

📒 Contact

If you have any questions or feedback, please feel free to contact me ↗️.

🎉 Experience the **99.99%** accuracy and bring the power of deep learning 🧠 to your fingertips! 🫵