FoodRepo: An Open Food Repository of Barcoded Food Products

Lazzari, Gianrocco; Jaquet, Yannis; Kebaili, Djilani; Symul, Laura; Salathé, Marcel

doi:10.3389/fnut.2018.00057

Cited by 6 publications

(2 citation statements)

References 37 publications

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“…Each food item was linked to a nutritional value database containing 2’129 items built on the Swiss Food Composition Database [ 22 ], MenuCH data [ 23 ], and Ciqual [ 24 ]. When food intake was logged through barcode scanning, nutritional values of the food items were fetched from the Open FoodRepo database API [ 25 ]. Manual entries were matched to food items by the annotators.…”

Section: Methodsmentioning

confidence: 99%

Food & You: A digital cohort on personalized nutrition

Héritier,

Allémann,

Balakiriev

et al. 2023

PLOS Digit Health

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Nutrition is a key contributor to health. Recently, several studies have identified associations between factors such as microbiota composition and health-related responses to dietary intake, raising the potential of personalized nutritional recommendations. To further our understanding of personalized nutrition, detailed individual data must be collected from participants in their day-to-day lives. However, this is challenging in conventional studies that require clinical measurements and site visits. So-called digital or remote cohorts allow in situ data collection on a daily basis through mobile applications, online services, and wearable sensors, but they raise questions about study retention and data quality. “Food & You” is a personalized nutrition study implemented as a digital cohort in which participants track food intake, physical activity, gut microbiota, glycemia, and other data for two to four weeks. Here, we describe the study protocol, report on study completion rates, and describe the collected data, focusing on assessing their quality and reliability. Overall, the study collected data from over 1000 participants, including high-resolution data of nutritional intake of more than 46 million kcal collected from 315,126 dishes over 23,335 participant days, 1,470,030 blood glucose measurements, 49,110 survey responses, and 1,024 stool samples for gut microbiota analysis. Retention was high, with over 60% of the enrolled participants completing the study. Various data quality assessment efforts suggest the captured high-resolution nutritional data accurately reflect individual diet patterns, paving the way for digital cohorts as a typical study design for personalized nutrition.

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Section: Methodsmentioning

confidence: 99%

Food & You: A digital cohort on personalized nutrition

Héritier,

Allémann,

Balakiriev

et al. 2023

PLOS Digit Health

Self Cite

View full text Add to dashboard Cite

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“…We prepared a trustable real dataset for the barcode recognition that has 3 standard subsets: training set, validation set, and test set as usual. The dataset was extended from the [14] work, and our new collecting campaign (also carefully double-check and annotate bounding box) from real image captures and 2 crawling online on product tagging websites: world.openfoodfacts.org & foodrepo.org [33]. Those images are EAN-13 single-barcode and various in size and format.…”

Section: A Datasetsmentioning

confidence: 99%

QuickBrowser: A Unified Model to Detect and Read Simple Object in Real-time

Do¹,

Kim²

2021

Preprint

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There are many real-life use cases such as barcode scanning or billboard reading where people need to detect objects and read the object contents. Commonly existing methods are first trying to localize object regions, then determine layout and lastly classify content units. However, for simple fixed structured objects like license plates, this approach becomes overkill and lengthy to run. This work aims to solve this detect-and-read problem in a lightweight way by integrating multi-digit recognition into a one-stage object detection model. Our unified method not only eliminates the duplication in feature extraction (one for localizing, one again for classifying) but also provides useful contextual information around object regions for classification. Additionally, our choice of backbones and modifications in architecture, loss function, data augmentation and training make the method robust, efficient and speedy. Secondly, we made a public benchmark dataset of diverse real-life 1D barcodes for a reliable evaluation, which we collected, annotated and checked carefully. Eventually, experimental results prove the method's efficiency on the barcode problem by outperforming industrial tools in both detecting and decoding rates with a real-time fps at a VGA-similar resolution. It also did a great job expectedly on the license-plate recognition task (on the AOLP dataset) by outperforming the current state-of-the-art method significantly in terms of recognition rate and inference time.

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