FoodAI: Food Image Recognition via Deep Learning for Smart Food Logging

Abstract

An important aspect of health monitoring is effective logging of food consumption. This can help management of diet-related diseases like obesity, diabetes, and even cardiovascular diseases. Moreover, food logging can help fitness enthusiasts, and people who wanting to achieve a target weight. However, food-logging is cumbersome, and requires not only taking additional effort to note down the food item consumed regularly, but also sufficient knowledge of the food item consumed (which is difficult due to the availability of a wide variety of cuisines). With increasing reliance on smart devices, we exploit the convenience offered through the use of smart phones and propose a smart-food logging system: FoodAI, which offers state-of-the-art deep-learning based image recognition capabilities. FoodAI has been developed in Singapore and is particularly focused on food items commonly consumed in Singapore. FoodAI models were trained on a corpus of 400,000 food images from 756 different classes. In this paper we present extensive analysis and insights into the development of this system. FoodAI has been deployed as an API service and is one of the components powering Healthy 365, a mobile app developed by Singapore's Heath Promotion Board. We have over 100 registered organizations (universities, companies, start-ups) subscribing to this service and actively receive several API requests a day. FoodAI has made food logging convenient, aiding smart consumption and a healthy lifestyle.

Published at KDD 2019 (Applied Data Science track). Demo is accessible at https://foodai.org/

References in corpus (9)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Caffe: Convolutional Architecture for Fast Feature Embedding
• DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition
• Unsupervised Domain Adaptation by Backpropagation
• Squeeze-and-Excitation Networks
• You Tweet What You Eat: Studying Food Consumption Through Twitter
• Does Journaling Encourage Healthier Choices? Analyzing Healthy Eating Behaviors of Food Journalers
• #FoodPorn: Obesity Patterns in Culinary Interactions
• Kissing Cuisines: Exploring Worldwide Culinary Habits on the Web

Cited by in corpus (8)

• Leveraging Automatic Personalised Nutrition: Food Image Recognition Benchmark and Dataset based on Nutrition Taxonomy
• AI4Food-NutritionFW: A Novel Framework for the Automatic Synthesis and Analysis of Eating Behaviours
• Large Scale Visual Food Recognition
• Biased Bytes: On the Validity of Estimating Food Consumption from Digital Traces
• SalienTrack: providing salient information for semi-automated self-tracking feedback with model explanations
• A Large-Scale Benchmark for Food Image Segmentation
• Photozilla: A Large-Scale Photography Dataset and Visual Embedding for 20 Photography Styles
• FoodLogAthl-218: Constructing a Real-World Food Image Dataset Using Dietary Management Applications