🖼️ Image Classification by Machine Learning Using Python (AICTE Internship Project)

This project is a Streamlit application designed to perform image classification using two powerful machine learning models: MobileNetV2 and a custom CIFAR-10 model. The application allows users to upload images and receive predictions along with confidence scores. It features a user-friendly navigation bar for seamless switching between models and delivers real-time results. This app is ideal for educational purposes, showcasing the performance of state-of-the-art models, and practical use in various image classification scenarios.

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✨ Key Features

1. Dual Model Support

• 📱 MobileNetV2 (ImageNet):

  • Recognizes 1,000 different classes from the ImageNet dataset.
  • Includes everyday objects, animals, vehicles, and more.
  • Lightweight and optimized for mobile and web applications.

• 🖼️ Custom CIFAR-10 Model:

  • Specializes in classifying images into ten categories:
    • ✈️ Airplane, 🚗 automobile, 🐦 bird, 🐱 cat, 🦌 deer, 🐶 dog, 🐸 frog, 🐴 horse, 🚢 ship, and 🚚 truck.
  • Designed for smaller-scale image classification tasks.

2. Intuitive Interface

• 🔀 Sleek Navigation Bar:

  • Effortlessly switch between MobileNetV2 and CIFAR-10 models using a sidebar menu.

• ⏱️ Real-Time Predictions:

  • Upload images in JPG or PNG format and receive instant classification results.
  • Displays predictions along with confidence scores for better understanding.

3. Educational and Practical Benefits

• 📘 Educational Value:

  • Learn about deep learning models, their architecture, and real-world performance.
  • Experiment with two different model types for comparative analysis.

• 💡 Practical Applications:

  • Use the app for tasks requiring quick and accurate image classification.

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🚀 Getting Started

🛠️ Prerequisites

• Python 3.7 or later.
• A modern web browser to access the application.

⚙️ Installation

Follow the steps below to set up and run the application locally:

1. 📥 Clone the Repository:

   git clone https://github.com/wthvishal/Image-Classification-By-Machine-Learning-Using-Python-AICTE-Internship-Project.git
   cd Image-Classification-By-Machine-Learning-Using-Python-AICTE-Internship-Project

2. 🔧 Create and Activate a Virtual Environment:

   python -m venv venv
   source venv/bin/activate   # On Windows use `venv\Scripts\activate`

3. 📦 Install Dependencies:

   pip install -r requirements.txt

4. ▶️ Run the Application:

   streamlit run app.py

5. 🌐 Open the App:

   • The app will automatically open in your default web browser.
   • If it doesn’t, navigate to http://localhost:8501.

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📖 Usage Instructions

1. 🔘 Select the Model:

   • Use the navigation bar to choose either the MobileNetV2 or CIFAR-10 model.

2. 📤 Upload an Image:

   • Click on the upload button to select a JPG or PNG image file.

3. 📊 View Results:

   • Observe the classification results and confidence scores displayed on the screen.

4. 🔄 Switch Models:

   • Easily switch between models to compare their performance on the same image.

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📂 File Structure

Image-Classification-By-Machine-Learning-Using-Python-AICTE-Internship-Project/
├── app.py               # Main Streamlit application file.
├── models/              # Directory containing model files and utilities.
├── requirements.txt     # Python dependencies.
├── README.md            # Project documentation.
└── assets/              # Additional resources (images, icons, etc.).

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🔧 Technologies Used

• Streamlit: For creating an interactive and user-friendly web application.
• TensorFlow/Keras: For implementing and loading deep learning models.
• Python: The core programming language used for development.

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🤝 Support This Project

• ⭐ Star the Repository: If you find this project helpful, please consider starring the repository on GitHub.
• 🔗 Share the Repository: Share it with others who might find it useful, especially students working on their internship projects or learning image classification.
• 👨‍💻 Follow Me on GitHub: Stay updated with my latest projects by following my GitHub profile: wthvishal.

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🌟 Contributing

We welcome contributions to improve this project! Follow these steps to get involved:

1. Fork the repository.
2. Create a new branch for your feature or bug fix:

   git checkout -b feature-name

3. Commit your changes:

   git commit -m "Add feature-name"

4. Push your branch:

   git push origin feature-name

5. Open a pull request on GitHub.

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🙏 Acknowledgements

• Streamlit: For providing an amazing framework for building interactive applications.
• TensorFlow: For enabling efficient implementation of deep learning models.
• AICTE Internship: For inspiring this project.

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📜 License

This project is licensed under the MIT License.

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📧 Contact

For any queries or suggestions, feel free to reach out:

• GitHub: wthvishal
• Email: soon