🍲 Food Vision (Food Image Classification)

This project demonstrates an image classification application using TensorFlow and Streamlit. The model is trained to classify images of various Indian food items.

📋 Table of Contents

• 📦 Installation
• 🚀 Usage
• 🔍 Model Training
  • ResNet50
  • EfficientNet
• 🤖 Model Prediction
• 📁 Project Structure
• 🙏 Acknowledgements

📦 Installation

Clone the repository:

git clone https://github.com/omraj0/food-vision.git
cd food-vision

Install the required packages:

pip install -r requirements.txt

🚀 Usage

To run the Streamlit app locally, use the following command:

streamlit run app.py

🔍 Model Training

The model is based on two popular architectures: ResNet50 and EfficientNetV2B0, both of which are trained on a dataset of Indian food images.

ResNet50

ResNet50 is a deep residual network that helps to mitigate the vanishing gradient problem by using residual blocks. This model is highly effective for image classification tasks and has been pre-trained on the ImageNet dataset.

EfficientNet

EfficientNetV2B0 is a family of models that efficiently scale up the size of the network. It balances network depth, width, and resolution to achieve better performance with fewer parameters.

Training Process

• Data Preparation: The images are loaded and preprocessed using TensorFlow's tf.keras.preprocessing.image_dataset_from_directory method.
• Model Architecture: The model uses either ResNet50 or EfficientNetV2B0 as the base model with a custom top layer for classification into 80 classes.
• Training: The model is trained with data augmentation to improve its generalization capability. The training script can be found in the train.py file.

🤖 Model Prediction

The prediction script (img_classification.py) loads a trained model and makes predictions on new images. The load_and_prep_image function preprocesses the image, and the predict_data function makes the prediction and returns the predicted class.

📁 Project Structure

.
├── app.py                      # Streamlit app script
├── img_classification.py       # Image classification script
├── train.py                    # Model training script
├── requirements.txt            # Python packages required
└── README.md                   # Project README file

🙏 Acknowledgements

This project is inspired by the Food-101 dataset and uses the ResNet50 and EfficientNetV2B0 models from TensorFlow.