ashwinexe / FYP

Final Year Project

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An AI Based Nutrient Tracking and Analysis

2019 ECM IV A Batch 13

Food recognition deployed on streamlit with inception v3 backend

Deployed at Streamlit

In this project, we propose a novel system based on artificial intelligence (AI) to accurately estimate nutrient intake, by processing image pairs captured in day meal consumption. This allows sequential food segmentation, recognition, and estimation of the consumed food volume, permitting fully automatic estimation of the nutrient intake for each meal. For this development and evaluation of the system, a dedicated new database containing images and nutrient recipes of large variety of meals must be assembled and coupled.

Learning details

Block Diagram

Dataset Details and Classes

Data consists of 1.1GB of 16,600 images of different categories of food. the categories of food that can be classified are

- Bread
- Dairy Product
- Dessert
- Egg
- Fried Food
- Meat
- Noodles-pasta
- Rice
- Seafood
- Soup
- Vegetable-fruit

Dataset is obtained from kaggle

This project is made by:

  • Ashwin Kumar Uppala 19311A1901
  • Sanjana Reddy 19311A1908
  • Raveena Ganji 19311A1958

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Final Year Project


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