🍽️ Food Recognition Models with PyTorch

Welcome to the FoodNet project! This repository contains state-of-the-art food recognition models built using PyTorch and trained on the Food101 dataset. Easily classify food images into 101 delicious categories with modern deep learning techniques.

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🚀 Features

• PyTorch-based: Flexible, modular, and easy-to-extend codebase.
• Food101 Dataset: Trained and evaluated on the popular Food101 dataset.
• Data Augmentation: Advanced image augmentations for robust models.
• Custom Architectures: Includes TinyVGG and more.
• Jupyter Notebooks: Interactive experiments and visualizations.

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📂 Project Structure

FoodNet/
|── data/                  # contains datasets
|── notebooks/             # various notebooks used
|── models/                # models created in pth form
|── utils                  # utility functions
├── requirements.txt       # Python dependencies
├── setup.py               # Project setup
└── README.md              # Project documentation

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🥗 Dataset

• Food101: 101 food categories, 101,000 images.

• Downloaded and managed automatically via torchvision.

• Direct download URL (from official source):

  http://data.vision.ee.ethz.ch/cvl/food-101.tar.gz
  

  or

  wget -c http://data.vision.ee.ethz.ch/cvl/food-101.tar.gz

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🛠️ Installation

1. Clone the repository:

   git clone https://github.com/yourusername/FoodNet.git
   cd FoodNet

2. Create and activate a virtual environment (recommended):

   python -m venv mlvenv
   # On Windows:
   .\mlvenv\Scripts\activate

3. Install dependencies:

   pip install -r requirements.txt

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📊 Usage

• Run and explore the Jupyter notebooks in src/notebooks/ to train, evaluate, and visualize models.
• Example: tinyvgg.ipynb demonstrates training a TinyVGG model on Food101.

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🧑‍💻 Author

• Amal Varghese
  officialamalv2004@gmail.com

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⭐️ Contributing

Pull requests, issues, and suggestions are welcome! Feel free to fork the repo and submit improvements.

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📜 License

This project is licensed under the MIT License.

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