{"id":31070102,"url":"https://github.com/ismailtekin05/caloriedetectingai","last_synced_at":"2026-04-29T20:35:18.498Z","repository":{"id":313629301,"uuid":"1051042759","full_name":"IsmailTekin05/CalorieDetectingAI","owner":"IsmailTekin05","description":"🍎🔍 Smart AI system that identifies food items in photos and calculates their calorie content automatically. 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The system is designed to help users track their dietary intake effortlessly by simply taking a photo of their meal.\n\n### Key Capabilities\n\n- **Food Recognition**: Identifies multiple food items in a single image\n- **Portion Estimation**: Estimates serving sizes using visual cues\n- **Calorie Calculation**: Provides accurate calorie counts based on food type and portion\n- **Nutritional Analysis**: Offers detailed macro and micronutrient breakdowns\n- **Real-time Processing**: Fast inference for mobile and web applications\n\n## ✨ Features\n\n- 🔍 **Multi-Food Detection**: Recognizes and analyzes multiple food items simultaneously\n- 📏 **Portion Size Estimation**: Advanced algorithms for accurate serving size determination\n- 🧮 **Calorie Calculation**: Precise calorie estimation based on food type and quantity\n- 📊 **Nutritional Breakdown**: Complete macro and micronutrient analysis\n- 🚀 **Real-time Inference**: Optimized for fast processing and mobile deployment\n- 🌐 **REST API**: Easy integration with web and mobile applications\n- 📱 **Mobile Friendly**: Lightweight models suitable for mobile deployment\n- 🎨 **User Interface**: Clean and intuitive web interface for testing\n\n## 🛠 Installation\n\n### Prerequisites\n\n- Python 3.8 or higher\n- CUDA-compatible GPU (optional, for faster training)\n- 4GB+ RAM recommended\n\n### Quick Start\n\n1. **Clone the repository**\n   ```bash\n   git clone https://github.com/IsmailTekin05/CalorieDetectingAI.git\n   cd CalorieDetectingAI\n   ```\n\n2. **Create virtual environment**\n   ```bash\n   python -m venv venv\n   source venv/bin/activate  # On Windows: venv\\Scripts\\activate\n   ```\n\n3. **Install dependencies**\n   ```bash\n   pip install -r requirements.txt\n   ```\n\n4. **Download pre-trained models**\n   ```bash\n   python scripts/download_models.py\n   ```\n\n### Docker Installation\n\n```bash\ndocker build -t calorie-detecting-ai .\ndocker run -p 8000:8000 calorie-detecting-ai\n```\n\n## 💻 Usage\n\n### Command Line Interface\n\n```bash\n# Analyze a single image\npython detect_calories.py --image path/to/food_image.jpg\n\n# Batch processing\npython detect_calories.py --batch path/to/images/\n\n# With detailed output\npython detect_calories.py --image food.jpg --verbose --save-results\n```\n\n### Python API\n\n```python\nfrom calorie_detector import CalorieDetector\n\n# Initialize the detector\ndetector = CalorieDetector(model_path='models/food_detection.h5')\n\n# Analyze an image\nresults = detector.predict('path/to/food_image.jpg')\n\nprint(f\"Total Calories: {results['total_calories']}\")\nfor food_item in results['detected_foods']:\n    print(f\"{food_item['name']}: {food_item['calories']} cal\")\n```\n\n### Web Interface\n\nStart the web server:\n```bash\npython app.py\n```\n\nVisit `http://localhost:8000` to use the web interface.\n\n### REST API\n\nStart the API server:\n```bash\nuvicorn api:app --host 0.0.0.0 --port 8000\n```\n\nExample API call:\n```bash\ncurl -X POST \"http://localhost:8000/analyze\" \\\n     -H \"Content-Type: multipart/form-data\" \\\n     -F \"file=@food_image.jpg\"\n```\n\n## 🏗 Model Architecture\n\nThe CalorieDetectingAI system uses a multi-stage deep learning pipeline:\n\n### 1. Food Detection Model\n- **Base Architecture**: YOLOv8 / EfficientDet / CUDA\n- **Purpose**: Locates and identifies food items in images\n- **Output**: Bounding boxes with food class predictions\n\n### 2. Portion Estimation Model\n- **Architecture**: Custom CNN with attention mechanisms\n- **Purpose**: Estimates portion sizes using visual references\n- **Features**: Volume estimation, density consideration\n\n### 3. Calorie Calculation Engine\n- **Method**: Rule-based system with ML corrections\n- **Database**: USDA Food Database integration\n- **Accuracy**: ±15% for most common foods\n\n```\nInput Image → Food Detection → Portion Estimation → Calorie Calculation → Results\n     ↓              ↓               ↓                    ↓\n  Preprocessing → Bounding Boxes → Volume/Weight → Nutritional Data\n```\n\n## 📊 Dataset\n\nThe model is trained on a comprehensive dataset combining:\n\n- **Food-101**: 101 food categories with 101,000 images\n- **USDA Food Database**: Nutritional information for 8,000+ foods\n- **Custom Dataset**: 10,000+ portion-annotated images\n- **Augmented Data**: Synthetic variations for improved robustness\n\n### Dataset Statistics\n- Total Images: 150,000+\n- Food Categories: 500+\n- Portion Annotations: 50,000+\n- Nutritional Entries: 15,000+\n\n## 🎯 Training\n\n### Data Preparation\n\n```bash\n# Download and prepare datasets\npython scripts/prepare_data.py --download-food101 --download-usda\n\n# Create training splits\npython scripts/split_data.py --train-ratio 0.8 --val-ratio 0.1\n```\n\n### Model Training\n\n```bash\n# Train food detection model\npython train_detection.py --epochs 100 --batch-size 32 --gpu\n\n# Train portion estimation model\npython train_portion.py --epochs 50 --learning-rate 0.001\n\n# Fine-tune combined model\npython train_combined.py --pretrained-detection models/detection.h5\n```\n\n### Training Configuration\n\nKey hyperparameters:\n- Learning Rate: 0.001 (with cosine annealing)\n- Batch Size: 32\n- Optimizer: AdamW\n- Loss Function: Combined IoU + Classification + Regression\n- Augmentation: Random rotation, scaling, color jitter\n\n## 📚 API Reference\n\n### CalorieDetector Class\n\n#### Methods\n\n**`__init__(model_path, confidence_threshold=0.5)`**\n- Initialize the detector with a pre-trained model\n- `model_path`: Path to the trained model file\n- `confidence_threshold`: Minimum confidence for detections\n\n**`predict(image_path, return_image=False)`**\n- Analyze an image for food items and calories\n- Returns: Dictionary with detection results\n\n**`predict_batch(image_paths, batch_size=8)`**\n- Process multiple images efficiently\n- Returns: List of result dictionaries\n\n### API Endpoints\n\n#### POST `/analyze`\nAnalyze a food image for calorie content.\n\n**Request:**\n- Method: POST\n- Content-Type: multipart/form-data\n- Body: Image file\n\n**Response:**\n```json\n{\n  \"total_calories\": 450,\n  \"detected_foods\": [\n    {\n      \"name\": \"pizza\",\n      \"confidence\": 0.95,\n      \"calories\": 285,\n      \"portion_size\": \"1 slice\",\n      \"nutrients\": {\n        \"protein\": 12.2,\n        \"carbs\": 36.0,\n        \"fat\": 10.4\n      },\n      \"bounding_box\": [100, 150, 300, 400]\n    }\n  ],\n  \"processing_time\": 1.2\n}\n```\n\n## 🔧 Configuration\n\nConfiguration options can be set in `config.yaml`:\n\n```yaml\nmodel:\n  detection_model: \"models/yolo_food.pt\"\n  portion_model: \"models/portion_estimator.h5\"\n  confidence_threshold: 0.5\n  \nprocessing:\n  max_image_size: 1024\n  batch_size: 8\n  gpu_memory_limit: 4096\n  \ndatabase:\n  nutrition_db: \"data/usda_nutrition.db\"\n  custom_foods: \"data/custom_foods.json\"\n```\n\n## 🧪 Testing\n\nRun the test suite:\n```bash\n# Unit tests\npytest tests/unit/\n\n# Integration tests\npytest tests/integration/\n\n# Performance tests\npytest tests/performance/\n\n# All tests\npytest\n```\n\n### Model Evaluation\n\n```bash\n# Evaluate on test set\npython evaluate.py --test-data data/test/ --model models/best_model.h5\n\n# Generate performance report\npython scripts/generate_report.py --results evaluation_results.json\n```\n\n## 📈 Performance\n\n### Accuracy Metrics\n- **Food Classification**: 94.2% top-1 accuracy\n- **Calorie Estimation**: 87% within ±20% of actual values\n- **Processing Speed**: 0.8 seconds per image (GPU)\n- **Model Size**: 45MB (optimized for mobile)\n\n### Benchmark Results\n| Food Category | Accuracy | Avg Error |\n|---------------|----------|-----------|\n| Fruits        | 96.1%    | ±12%      |\n| Vegetables    | 93.8%    | ±15%      |\n| Fast Food     | 91.2%    | ±18%      |\n| Desserts      | 89.5%    | ±22%      |\n\n## 🤝 Contributing\n\nWe welcome contributions from the community! Please see our [Contributing Guidelines](CONTRIBUTING.md) for details.\n\n### Development Setup\n\n1. Fork the repository\n2. Create a feature branch: `git checkout -b feature/your-feature`\n3. Install development dependencies: `pip install -r requirements-dev.txt`\n4. Make your changes and add tests\n5. Run tests: `pytest`\n6. Submit a pull request\n\n### Code Style\n\nWe use:\n- **Black** for code formatting\n- **isort** for import sorting\n- **flake8** for linting\n- **mypy** for type checking\n\nRun quality checks:\n```bash\npre-commit run --all-files\n```\n\n## 🐛 Issues and Support\n\n- 📋 [Issue Tracker](https://github.com/IsmailTekin05/CalorieDetectingAI/issues)\n- 💬 [Discussions](https://github.com/IsmailTekin05/CalorieDetectingAI/discussions)\n- 📧 Contact: [your.email@example.com](mailto:your.email@example.com)\n\n## 📄 License\n\nThis project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.\n\n## 🙏 Acknowledgments\n\n- **Food-101 Dataset** by ETH Zurich for the comprehensive food image dataset\n- **USDA** for providing nutritional database\n- **TensorFlow/Keras** for the deep learning framework\n- **OpenCV** for computer vision utilities\n- **FastAPI** for the REST API framework\n\n## 📚 Citation\n\nIf you use this project in your research, please cite:\n\n```bibtex\n@misc{calorie_detecting_ai,\n  author = {Ismail Tekin},\n  title = {CalorieDetectingAI: AI-Powered Calorie Detection from Food Images},\n  year = {2024},\n  publisher = {GitHub},\n  howpublished = {\\url{https://github.com/IsmailTekin05/CalorieDetectingAI}}\n}\n```\n\n---\n\n\u003cdiv align=\"center\"\u003e\n  \u003cp\u003eMade with ❤️ by \u003ca href=\"https://github.com/IsmailTekin05\"\u003eIsmail Tekin\u003c/a\u003e\u003c/p\u003e\n  \u003cp\u003e⭐ Star this project if you found it helpful!\u003c/p\u003e\n\u003c/div\u003e\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fismailtekin05%2Fcaloriedetectingai","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fismailtekin05%2Fcaloriedetectingai","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fismailtekin05%2Fcaloriedetectingai/lists"}