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README.md

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+---
+license: mit
+tags:
+- food-recognition
+- computer-vision
+- calorie-estimation
+- efficientnet
+- nutrition
+- health
+pipeline_tag: image-classification
+---
+
+# Food Recognition and Calorie Estimation Model
+
+A comprehensive deep learning system for food recognition, object detection, and calorie estimation using TensorFlow, YOLO, and EfficientNet.
+
+## Model Description
+
+This model combines multiple deep learning approaches to provide accurate food recognition and calorie estimation:
+
+- **Food Classification**: EfficientNet-B0 based multi-label classifier for 101 food categories
+- **Object Detection**: YOLO v8 for detecting multiple food items in images
+- **Portion Size Estimation**: Computer vision techniques for size estimation
+- **Calorie Calculation**: Integration with USDA nutritional database
+
+## Model Performance
+
+| Metric | Value |
+|--------|-------|
+| Classification Accuracy | >85% |
+| Object Detection mAP | >0.75 |
+| Calorie Estimation Accuracy | ±20% |
+| Inference Speed | <2 seconds/image |
+
+## Usage
+
+### Basic Usage
+
+```python
+from transformers import pipeline
+
+# Load the model
+classifier = pipeline("image-classification", model="BinhQuocNguyen/food-recognition-model")
+
+# Analyze a food image
+result = classifier("path/to/food_image.jpg")
+print(f"Detected foods: {result}")
+```
+
+### Advanced Usage
+
+```python
+import torch
+from transformers import AutoModel, AutoImageProcessor
+from PIL import Image
+
+# Load model and processor
+model = AutoModel.from_pretrained("BinhQuocNguyen/food-recognition-model")
+processor = AutoImageProcessor.from_pretrained("BinhQuocNguyen/food-recognition-model")
+
+# Process image
+image = Image.open("food_image.jpg")
+inputs = processor(images=image, return_tensors="pt")
+
+# Get predictions
+with torch.no_grad():
+    outputs = model(**inputs)
+    predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
+```
+
+## Training Data
+
+The model was trained on:
+- **Food-101 Dataset**: 101,000 images across 101 food categories
+- **Additional Datasets**: Food-11, Recipe1M+ (where available)
+- **Data Augmentation**: Rotation, flip, brightness, contrast adjustments
+
+## Nutritional Database
+
+The model includes nutritional information for 101 food categories with:
+- Calories per 100g
+- Protein, carbohydrate, and fat content
+- Portion size estimation capabilities
+
+## Limitations
+
+- Accuracy may vary with image quality and lighting conditions
+- Calorie estimates are approximate and should not replace professional dietary advice
+- Model performance depends on food items being within the trained categories
+- Portion size estimation is based on visual cues and may not be accurate for all cases
+
+## Citation
+
+```bibtex
+@misc{food-recognition-model,
+  title={Food Recognition and Calorie Estimation Model},
+  author={BinhQuocNguyen},
+  year={2024},
+  publisher={Hugging Face},
+  howpublished={\url{https://huggingface.co/BinhQuocNguyen/food-recognition-model}}
+}
+```
+
+## License
+
+This model is licensed under the MIT License.
+
+## Contact
+
+For questions or issues, please contact:
+- GitHub: [Food Recognition Repository](https://github.com/BinhQuocNguyen/food-recognition-model)
+
+## Acknowledgments
+
+- Food-101 dataset creators
+- TensorFlow team
+- Hugging Face team
+- USDA Food Database
+- OpenCV community