Add: [AI-ML] Image Classifier using Transfer Learning with MobileNetV2

Domains/AI-ML/MiniProjects/ImageClassifier/.gitignore

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+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# Virtual environment
+venv/
+env/
+ENV/
+.venv
+
+# Streamlit
+.streamlit/
+
+# TensorFlow cache
+.keras/
+*.h5
+*.pb
+*.ckpt
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# Image files (for testing)
+*.jpg
+*.jpeg
+*.png
+*.gif
+*.bmp
+
+# Model files (if saved locally)
+models/
+*.pkl
+*.joblib
+
+# IDE
+.vscode/
+.idea/
+*.swp
+*.swo
+
+# OS
+.DS_Store
+Thumbs.db
+
+# Logs
+*.log
+
+# Distribution / packaging
+dist/
+build/
+*.egg-info/

Domains/AI-ML/MiniProjects/ImageClassifier/README.md

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+# 🖼️ Image Classifier Using Transfer Learning
+
+**Contributor:** vatsalgupta2004  
+**Domain:** AI-ML  
+**Difficulty:** Intermediate  
+**Tech Stack:** Python, TensorFlow/Keras, MobileNetV2, Streamlit
+
+---
+
+## 📝 Description
+
+A powerful image classification application using Transfer Learning with MobileNetV2 pre-trained on ImageNet. The application features a beautiful web interface built with Streamlit that allows users to upload images and get instant predictions with confidence scores. Supports 1000+ object categories including animals, vehicles, objects, and more.
+
+---
+
+## 🎯 Features
+
+- ✅ **Pre-trained MobileNetV2 Model** - Leverages transfer learning for accurate predictions
+- ✅ **1000+ Categories** - Recognizes animals, vehicles, objects, food, and more
+- ✅ **Web Interface** - Beautiful Streamlit UI for easy interaction
+- ✅ **Real-time Predictions** - Instant classification with confidence scores
+- ✅ **Top-5 Predictions** - Shows the top 5 most likely categories
+- ✅ **Image Preprocessing** - Automatic image resizing and normalization
+- ✅ **Drag & Drop Upload** - Easy image upload interface
+- ✅ **Confidence Visualization** - Progress bars for prediction confidence
+- ✅ **Multiple Format Support** - JPG, PNG, JPEG supported
+- ✅ **Lightweight & Fast** - MobileNetV2 optimized for speed
+
+---
+
+## 🛠️ Tech Stack
+
+- **Python 3.8+** - Core programming language
+- **TensorFlow 2.x** - Deep learning framework
+- **Keras** - High-level neural networks API
+- **MobileNetV2** - Pre-trained CNN model
+- **Streamlit** - Web application framework
+- **Pillow** - Image processing
+- **NumPy** - Numerical computations
+
+---
+
+## 📋 Prerequisites
+
+- Python 3.8 or higher
+- pip package manager
+- 4GB+ RAM recommended
+- Internet connection (for first-time model download)
+
+---
+
+## 🚀 Installation
+
+1. **Clone the repository:**
+   ```bash
+   cd Domains/AI-ML/MiniProjects/ImageClassifier
+   ```
+
+2. **Create virtual environment (recommended):**
+   ```bash
+   python -m venv venv
+   source venv/bin/activate  # On Windows: venv\Scripts\activate
+   ```
+
+3. **Install dependencies:**
+   ```bash
+   pip install -r requirements.txt
+   ```
+
+---
+
+## 💻 Usage
+
+1. **Run the Streamlit app:**
+   ```bash
+   streamlit run app.py
+   ```
+
+2. **Open your browser:**
+   - The app will automatically open at `http://localhost:8501`
+   - Or manually navigate to the URL shown in terminal
+
+3. **Classify images:**
+   - Click "Browse files" or drag & drop an image
+   - Wait for instant predictions
+   - View top 5 predictions with confidence scores
+
+---
+
+## 📁 Project Structure
+
+```
+ImageClassifier/
+│
+├── app.py                 # Main Streamlit application
+├── classifier.py          # Image classification logic
+├── requirements.txt       # Python dependencies
+├── README.md             # Project documentation
+├── .gitignore            # Git ignore file
+│
+└── samples/              # Sample test images (optional)
+    ├── dog.jpg
+    ├── car.jpg
+    └── pizza.jpg
+```
+
+---
+
+## 🔧 How It Works
+
+1. **Model Loading:**
+   - Uses MobileNetV2 pre-trained on ImageNet dataset
+   - Includes top classification layer for 1000 categories
+   - Weights are downloaded automatically on first run
+
+2. **Image Preprocessing:**
+   - Resizes image to 224x224 pixels
+   - Normalizes pixel values to [-1, 1] range
+   - Applies MobileNetV2-specific preprocessing
+
+3. **Prediction:**
+   - Passes preprocessed image through the network
+   - Decodes predictions to human-readable labels
+   - Returns top-5 predictions with confidence scores
+
+4. **Visualization:**
+   - Displays uploaded image
+   - Shows predictions in descending confidence order
+   - Visualizes confidence with progress bars
+
+---
+
+## 📊 Supported Categories
+
+The model can classify **1000+ categories** including:
+
+- 🐕 **Animals:** Dogs, cats, birds, reptiles, insects
+- 🚗 **Vehicles:** Cars, trucks, airplanes, boats, bicycles
+- 🍕 **Food:** Pizza, burgers, fruits, vegetables, desserts
+- 🏠 **Objects:** Furniture, electronics, tools, clothing
+- 🌳 **Nature:** Trees, flowers, landscapes, weather
+- 🎸 **Instruments:** Guitars, pianos, drums, violins
+- 🏈 **Sports:** Balls, equipment, gear
+- And many more!
+
+---
+
+## 🎨 Example Use Cases
+
+1. **Educational Tool:** Learn about object recognition and AI
+2. **Content Moderation:** Automatically tag and categorize images
+3. **Photo Organization:** Auto-tag photos in your collection
+4. **Product Recognition:** Identify products from images
+5. **Wildlife Identification:** Recognize animals and plants
+6. **Quality Control:** Classify manufactured products
+7. **Research:** Study transfer learning and CNNs
+
+---
+
+## 🧪 Testing
+
+Try these sample images to test the classifier:
+
+- **Animals:** Upload pictures of pets, wildlife, or insects
+- **Vehicles:** Cars, bikes, planes, boats
+- **Food:** Restaurant dishes, fruits, vegetables
+- **Objects:** Household items, electronics, tools
+- **Nature:** Flowers, trees, landscapes
+
+---
+
+## 📈 Model Performance
+
+- **Architecture:** MobileNetV2 (Inverted Residuals)
+- **Parameters:** ~3.5 million
+- **Input Size:** 224x224x3
+- **Training Dataset:** ImageNet (1.2M images, 1000 classes)
+- **Top-1 Accuracy:** ~71.8% on ImageNet validation set
+- **Top-5 Accuracy:** ~90.8% on ImageNet validation set
+- **Inference Speed:** ~30-50ms per image (CPU)
+
+---
+
+## 🔮 Future Enhancements
+
+- [ ] Add custom model training capability
+- [ ] Support for video classification
+- [ ] Batch processing for multiple images
+- [ ] Export predictions to CSV/JSON
+- [ ] Confidence threshold filtering
+- [ ] Model selection (VGG, ResNet, EfficientNet)
+- [ ] Image augmentation preview
+- [ ] Deployment to cloud (Heroku, AWS, GCP)
+- [ ] Mobile app version
+- [ ] API endpoint creation
+
+---
+
+## 🐛 Troubleshooting
+
+**Issue:** Model download fails
+- **Solution:** Check internet connection, try again, or manually download weights
+
+**Issue:** Out of memory error
+- **Solution:** Close other applications, use smaller batch size, or upgrade RAM
+
+**Issue:** Slow predictions
+- **Solution:** Use GPU if available, or consider using MobileNetV2 Alpha=0.5 for faster inference
+
+**Issue:** Incorrect predictions
+- **Solution:** Ensure good image quality, proper lighting, and clear object visibility
+
+---
+
+## 📚 Learning Resources
+
+- [TensorFlow Documentation](https://www.tensorflow.org/api_docs)
+- [MobileNetV2 Paper](https://arxiv.org/abs/1801.04381)
+- [Transfer Learning Guide](https://www.tensorflow.org/tutorials/images/transfer_learning)
+- [ImageNet Dataset](https://www.image-net.org/)
+- [Streamlit Documentation](https://docs.streamlit.io/)
+
+---
+
+## 🤝 Contributing
+
+Contributions are welcome! Feel free to:
+- Add new features
+- Improve model performance
+- Enhance UI/UX
+- Fix bugs
+- Add documentation
+
+---
+
+## 📄 License
+
+This project is created for **Hacktoberfest 2025** and educational purposes.
+
+---
+
+## 🙏 Acknowledgments
+
+- **TensorFlow Team** - For the amazing deep learning framework
+- **Google** - For MobileNetV2 architecture
+- **ImageNet** - For the comprehensive dataset
+- **Streamlit** - For the intuitive web framework
+- **Hacktoberfest 2025** - For promoting open source
+
+---
+
+## 📧 Contact
+
+Created by **vatsalgupta2004** for Hacktoberfest 2025
+
+- GitHub: [@vatsalgupta2004](https://github.com/vatsalgupta2004)
+- Project: [ProjectHive](https://github.com/vatsalgupta2004/ProjectHive)
+
+---
+
+**⭐ If you find this project helpful, please give it a star!**