Food image prediction using TensorFlow and calorie estimation using K-Nearest-Neighbors algoritm

An ML model which is trained to recognise food images, calculate volume and estimate calorie content

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基于YOLO的食物卡路里检测系统(源码＆部署教程＆数据集)

Food Ingredients Recognition and Calorie Estimation using Multi-Task Deep Learning

Food calorie estimations Using Deep Learning And Computer Vision

Transformer based YOLO v5 model for food image localization for 36 distinct food classes and giving better results than the YOLO v7 model

Food Calorie Estimation using Deep Learning and AR

Food calorie estimation integrated with ImageNet and google generative AI with flask API and Basic Frontend using Tailwind

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CalorieCaptorGlass : Food Calorie Estimation based on Actual Size using HoloLens and Deep Learning (IEEE VR 2020 Demo)

The Calorie Estimation Project can be mainly divided into two parts, identifying food from image, and estimating calorie from certain food image. For the identification of food image, we performed multi-class SVM algorithms, with different features explored and compared, including HOG (Histogram of Gradients), LBP (Linear Binary Pattern) and CNN. The result shows that the local feature LBP performs the best overall. The food calorie data from Internet is collected to conclude a table for easy conversion from food category to calorie.

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[2022-1 SSU ML & AI Term Project] Food Calorie Estimation : Food detection → Volume estimation → Calorie, Nutrients Calculation → Results

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Food classification using Food 101 dataset and its calorie estimation GUI

Deep Learning based Ingredient Recognition, Food categorization, Vegan Non Vegan classification, and Calorie Estimation

Project to detect food from image and predict the calories.

Website which takes food image as input and classifies food and estimates he amount of calories present in it.

This Project estimate Calories from fast food images using Deep learning, Machine learning Algorithms and Models. Android app is used for client side and flask server to deploy ML Classifier.

An AI-powered system that detects food items from images and estimates their calories using deep learning, enabling efficient dietary tracking and health monitoring.

Built a computer vision regression pipeline with PyTorch, ResNet-50, and ViT to estimate total meal calories from single overhead images. Includes Colab training notebooks, clean data loaders, evaluation metrics/plots, and a reusable CLI inference script for reproducible experiments.

Food recognition and calorie estimation uses AI to identify food items in images and estimate their calorie content. By analyzing a photo, the system detects different foods, estimates portion sizes, and calculates calories. It’s useful for diet tracking, health monitoring, and fitness apps.

A calorie tracking skill for the Yandex Alice voice assistant, built during the Yandex Hackathon 2022. Leverages AI for intelligent food recognition and calorie estimation.

In the current age, people are more conscious about their food and diet to avoid either upcoming or existing diseases.In order to properly assess dietary intake, accurate estimation of calorie value of food is of paramount importance.Due to the advances in various technologies used in smart phones, their computational power has also increased.They are capable of processing real-time multi-media information with their computational power. Since the present smart phones can handle the high-quality images too, research on food classification is focused on developing real-time applications which capture images and train the machine learning models instantly.It helps to take prevention to avoid diseases such as diabetes, blood pressure and so on.

Lightweight AI Food Detection & Calorie Estimation Demo

A multimodal deep learning system for food calorie estimation, fine-tuned on Qwen3-VL-4B using LoRA. Developed for CUHK AIMS5701.

A simple Flask application that predicts food type from an uploaded image and estimates its calorie content using a pre-trained model on the Food-101 dataset. The project includes image preprocessing, model training with MobileNetV2, and a web interface for uploading images and displaying results.

In this project, we tried to realize a deep learning application for the classification and recognition of foods, and an implementation of some methods of image segmentation whose aim is to give an estimate of the weight of a food in an image taken by the user. To achieve such a result, the goal of this work is to achieve the following objectives: 1. Create a CNN-type neural network for meal recognition from a captured image. 2. Find a way to estimate the number of calories in a meal from a picture.

AI-powered food recognition and calorie estimation system using deep learning for dietary tracking.