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Image Classification using Convolutional Neural Networks (CNN)

🎯 Project Overview

This project implements an advanced image classification system using Convolutional Neural Networks (CNNs) with transfer learning. The model uses data augmentation techniques and pre-trained deep learning models to achieve high accuracy on image classification tasks.

🚀 Technologies Used

  • Python - Core programming language
  • TensorFlow - Deep learning framework
  • Keras - High-level neural networks API
  • OpenCV - Computer vision library
  • NumPy - Numerical computing
  • Matplotlib - Data visualization
  • Jupyter Notebook - Interactive development

✨ Key Features

  • Advanced CNN Architecture - Custom convolutional neural network design
  • Transfer Learning - Leveraging pre-trained MobileNetV2 model
  • Data Augmentation - Image rotation, zoom, flip for better generalization
  • Model Optimization - Fine-tuned hyperparameters for optimal performance
  • Real-time Prediction - Ready for deployment and real-world applications
  • Comprehensive Evaluation - Detailed accuracy analysis and visualization

📁 Project Structure

image-classification-cnn/
│
├── data/
│   ├── train/
│   ├── validation/
│   └── test/
│
├── notebooks/
│   └── cnn_image_classification.ipynb
│
├── models/
│   └── trained_model.h5
│
├── src/
│   ├── data_preprocessing.py
│   ├── model.py
│   ├── train.py
│   └── evaluate.py
│
├── requirements.txt
├── README.md
└── .gitignore

🛠️ Installation & Setup

  1. Clone the repository
git clone https://github.com/yourusername/image-classification-cnn.git
cd image-classification-cnn
  1. Install dependencies
pip install -r requirements.txt
  1. Download dataset
  • The project uses Cats vs Dogs dataset
  • Dataset will be automatically downloaded when running the notebook

🚀 Usage

Option 1: Jupyter Notebook (Recommended)

jupyter notebook notebooks/cnn_image_classification.ipynb

Option 2: Python Scripts

# Train the model
python src/train.py

# Evaluate the model
python src/evaluate.py

📊 Model Architecture

  • Base Model: MobileNetV2 (pre-trained on ImageNet)
  • Transfer Learning: Frozen base layers + custom classifier
  • Input Shape: 224x224x3 RGB images
  • Output: Binary classification (Cats vs Dogs)
  • Optimizer: Adam with learning rate 0.0001

📈 Results

  • Training Accuracy: ~95%
  • Validation Accuracy: ~92%
  • Model Size: ~9MB (optimized for deployment)

🔮 Future Enhancements

  • Multi-class classification support
  • Model deployment using Flask/FastAPI
  • Real-time webcam classification
  • Mobile app integration
  • Docker containerization

🤝 Contributing

  1. Fork the repository
  2. Create your feature branch (git checkout -b feature/AmazingFeature)
  3. Commit your changes (git commit -m 'Add some AmazingFeature')
  4. Push to the branch (git push origin feature/AmazingFeature)
  5. Open a Pull Request

📄 License

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

👨‍💻 Author

Your Name

🙏 Acknowledgments

  • TensorFlow team for the amazing framework
  • Keras for the high-level API
  • Google for the pre-trained models