Popular GitHub Repositories for Food Diary

All-in-one productivity app and AI assistant with Tasks, Notes, Calendar, Diary and Bookmarks.

? A practical and imaginary food delivery microservices, built with .Net 8, MassTransit, Domain-Driven Design, CQRS, Vertical Slice Architecture, Event-Driven Architecture, and the latest technologies.

Food Classification with Deep Learning in Keras / Tensorflow

food image to recipe with deep convolutional neural networks.

SparkyFitness: Built for Families. Powered by AI. Track food, fitness, water, and health — together.

This repository contains the code to generate predictions of critical violations at food establishments in Chicago. It also contains the results of an evaluation of the effectiveness of those predictions.

This is a tracking repo for all our AI projects. ? ??

Food/Diet Recommendation system using machine learning

A CoreML model which classifies images of food

Deep Learning model to classify food (Web App)

KitcheNette: Predicting and Recommending Food Ingredient Pairings using Siamese Neural Networks

The official repository implement of Res-VMamba: Fine-Grained Food Category Visual Classification Using Selective State Space Models with Deep Residual Learning

一个使用Flutter开发，支持诸多云平台AI大模型API调用的智能工作生活助手应用。除了常规大模型应用，还有极简记账、随机菜品、猫狗之家、waifu图片、MAL动漫排行、BGM动漫资讯、饮食健康等生活日常工具。

Code & data accompanying the WSDM 2021 paper "Personalized Food Recommendation as Constrained Question Answering over a Large-scale Food Knowledge Graph"

FoodBERT: Food Extraction with DistilBERT

?? This is a module to make specifics OCRs at food products and nutritional tables.

2017年印度食物成分表（IFCT 2017）工具包，包含528种主要食物的营养成分值。

Food Ordering Chatbot

sentiment analysis on Snapp!food users' comments using classical machine learning models and LSTM mdoels

? This project analyzes Amazon Fine Food Reviews to investigate whether negative reviews are more emotionally intense and lexically repetitive than positive ones. Using R, we apply sentiment analysis and lexical diversity metrics to uncover patterns in consumer review language.