This repository contains a computer vision project that utilizes deep learning techniques to classify images of food into 101 different categories. The project is implemented using Jupyter Notebooks and aims to provide a comprehensive solution for food image recognition.
The main objective of this project is to develop a model that can accurately classify food images into 101 distinct categories. This can be beneficial for various applications such as automated menu recognition, dietary tracking, and culinary content analysis.
- Deep Learning Models: Utilizes state-of-the-art deep learning techniques for image classification.
- Jupyter Notebooks: Interactive notebooks that allow for easy experimentation and visualization.
- Comprehensive Dataset: Uses the Food-101 dataset, which contains 101,000 images of food categorized into 101 classes.
To get started with this project, clone the repository and install the required dependencies:
git clone https://github.com/abhi24112/Food101_Vision_Project-.git
cd Food101_Vision_Project-Contributions are welcome! If you find any issues or have suggestions for improvements, please open an issue or submit a pull request.