khaledTarek

Food Classification

Description

In this project, I developed a cutting-edge FoodVision application utilizing advanced deep learning models, specifically EfficientNet-B1 and Vision Transformer (ViT) architectures. By leveraging these pre-trained models, the app effectively classifies and identifies various food types with high accuracy. Users can effortlessly upload images of food, and the app processes these images to deliver precise predictions about the dish.

GitHub

Tools Used

pytorch




My Role



1- Understanding Requirements:


1.1- Who is the audience?

  • Food Enthusiasts and Chefs: Individuals who are passionate about food and cooking can use the app to identify and learn more about different dishes and ingredients. They seek an easy way to recognize and categorize various food items.

  • Restaurants and Food Industry Professionals: These users need a reliable tool for menu planning, quality control, and ensuring the accuracy of dish identification. The app assists them in verifying dishes and integrating accurate food classifications into their operations.



1.2- What are their goals and objectives?


Goals/Objectives:

  • Quickly and accurately identify a wide range of food dishes.

  • Enhance the user experience by providing instant and reliable food classification.

  • Improve menu accuracy and consistency in food-related applications.



Pain Points:

  • Difficulty in identifying dishes accurately from images.

  • Lack of efficient tools for food classification that integrate seamlessly into food-related applications.



Expectations from the App:

  • The ability to upload food images and receive immediate, accurate predictions.

  • A user-friendly interface that simplifies the process of food classification.



2- Data and Modeling

The app utilizes pre-trained deep learning models based on EfficientNet-B1 and Vision Transformer (ViT) architectures. The models were trained on a large dataset of food images to enhance accuracy in recognizing various types of cuisine and dishes. Minimal additional data cleansing was required due to the robustness of the pre-trained models.

3- Features

Key features of the FoodVision app include:

  • Image Upload and Processing: Users can upload images of food, which are then processed by the app to identify the type of dish and provide predictions.

  • Deep Learning Models: The app leverages EfficientNet-B1 and ViT architectures, known for their superior accuracy and efficiency compared to traditional convolutional neural networks (CNNs).

  • User-Friendly Interface: Designed to be intuitive and easy to navigate, allowing users to classify food images quickly and efficiently.