The Food and Food Categories (FFoCat) Dataset

The Food and Food Categories (FFoCat) Dataset contains 58.962 images of food annotated with the food label and the food categories of the Mediterranean Diet. It is one of the most complete datasets regarding the Mediterranean Diet as it is aligned with the standard AGROVOC and HeLiS ontologies and allows to study multitask learning problems in Computer Vision for food recognition and diet recommendation.

The dataset is already divided into the train and test folder. The file label.tsv contains the food labels, the file food_food_category_map.tsv contains the food labels with the corresponding food category labels. The following table compares the FFoCat dataset with previous datasets for food recognition.

This dataset has been published at the International Conference on Image Analysis and Processing (ICIAP - 2019). The source code for reproducing the experiments together with other information about the dataset is available here.

AGROVOC Alignment of Food Categories

The AGROVOC_alignment.tsv file contains the alignment of the food categories in the FFoCat dataset with AGROVOC, the standard ontology of the Food and Agriculture Organization (FAO) of the United Nations. This allows interoperability and linked open data navigation. Such alignment can be derived by querying HeLis, here we propose a shortcut.

Citing FFoCat

If you use FFoCat in your research, please use the following BibTeX entry.

@inproceedings{DonadelloD19Ontology, author = {Ivan Donadello and Mauro Dragoni}, title = {Ontology-Driven Food Category Classification in Images}, booktitle = {{ICIAP} {(2)}}, series = {Lecture Notes in Computer Science}, volume = {11752}, pages = {607--617}, publisher = {Springer}, year = {2019} }

This file contains the data elements used for searching the FDA Online Data Repository including proprietary name, active ingredients, marketing application number or regulatory citation, National Drug Code, and company name.

Take food labeling seriously......................

Data generated from an in-store choice experiment with shoppers on generic, identity-priming, or social norms-based labels highlighting healthy foods.

This dataset provides the hand-labelled crop / non-crop points used for training, which were created by labelling high-resolution satellite imagery in QGIS and Google Earth Pro. Data is available for Ethiopia, Sudan, Togo and Kenya.

Code used to process these points is available in the following github repository: https://github.com/nasaharvest/crop-maml

For more information, or if you use any part of this dataset, please refer to / cite the following paper: Gabriel Tseng, Hannah Kerner, Catherine Nakalembe and Inbal Becker-Reshef. 2021. Learning to predict crop type from heterogeneous sparse labels using meta-learning. GeoVision Workshop at CVPR ’21: June 19th, 2021

The generated dataset is an annotated collection, with each image carrying labels (NutriScore, V-label and Bio). The presence of annotated data is essential for developing a supervised machine-learning model capable of automatically identifying labels in new images. In our case, we utilize this data to train a model that can autonomously recognize labels on new images not present in the dataset, achieving a model accuracy of 94%. In the future, you have the option to train a new model using the dataset to achieve higher accuracy or employ the existing model to automatically identify bio and nutri labels in newly collected images, eliminating the need for manual review. We should emphasize that these resources should be utilized by a data science team. There is an opportunity for this model to be integrated with a mobile app, but this is a direction for future work, we included in the revised version.

In this research, we introduce the NutriGreen dataset, which is a collection of images representing packaged food products. Each image in the dataset comes with three distinct labels: one indicating its nutritional value using the Nutri-Score, another denoting whether it's vegan or vegetarian with the V-label, and a third displaying the EU organic certification (BIO) logo. The dataset comprises a total of 10,472 images. Among these, the Nutri-Score label is distributed across five sub-labels: A with 1,250 images, B with 1,107 images, C with 867 images, D with 1,001 images, and E with 967 images. Additionally, there are 870 images featuring the V-Label, 2,328 images showcasing the BIO label, and 3201 images with no labels. Furthermore, we have fine-tuned the YOLOv5 model to demonstrate the practicality of using these annotated datasets, achieving an impressive accuracy of 94.0%. These promising results indicate that this dataset has significant potential for training innovative systems capable of detecting food labels. Moreover, it can serve as a valuable benchmark dataset for emerging computer vision systems.

The global food date rotation label market is projected to reach a valuation of XXX million by 2033, exhibiting a CAGR of XX% during the forecast period (2025-2033). The market growth is primarily driven by the increasing demand for food safety and quality, stringent government regulations on food labeling, and the growing adoption of food date rotation practices in various industries. The market is further segmented by application, including restaurants, grocery stores and supermarkets, food manufacturing and processing, home and personal use, and others. In terms of types, the market is categorized into removable and permanent labels. Key market players include Ecolab, Cambro, DotIt, DayMark, National Checking, Noble Products, Avery, 3 Sigma, Buzz, Hubert Brand, LabelFresh, Able Label, Great Lakes Label, and Avery Dennison. The market is expected to witness significant growth in the Asia Pacific region due to the increasing awareness of food safety and the rapid adoption of food date rotation practices in the region.

We're making improvements to the nutrition facts table and list of ingredients on food labels based on feedback from Canadians and stakeholders. The food industry has a transition period of 5 years to make these changes. This means that you might start seeing new food labels as early as 2017.

Labelling on food helps Canadians make healthy and informed choices about the foods they buy and eat.

The percentage of qualified food labeling on the market.

The documentation below is in reference to this items placement in the NM Supply Chain Data Hub. The documentation is of use to understanding the source of this item, and how to reproduce it for updatesTitle: FOOD LABELS EXPOSED A definitive guide to common food label terms and claimsItem Type: pdfSummary: Definitions of common food labels and certifications and verifying organizations.Notes: Prepared by: Uploaded by EMcRae_NMCDCSource: https://agreenerworld.org/certifications/animal-welfare-approved/Feature Service: https://nmcdc.maps.arcgis.com/home/item.html?id=64388624b30242298d4133a61289ac58#UID: 24Data Requested: https://agreenerworld.org/certifications/animal-welfare-approved/Method of Acquisition: Downloaded from public website A Greener WorldDate Acquired: 6/23/22Priority rank as Identified in 2022 (scale of 1 being the highest priority, to 11 being the lowest priority): 5Tags: PENDING

Background. Understanding consumers’ interpretation of allergy information is crucial for effective food safety policies. We evaluated consumer understanding of allergy information on foods in controlled, experimental studies.Method. Using 18 packaged foods, we evaluated consumer understanding of information about allergens in 2 experiments. First, a comparison of foods with no stated allergen versus allergen as a stated ingredient versus a Precautionary Allergen Label (PAL); second, a comparison of 3 common variants of PAL. In each experiment, consumers with and without self-reported food allergy were asked to estimate the risk of allergic reaction and to rate the comprehensibility of the allergen information. In the second experiment, consumers were also asked which form of PAL they preferred.Results. Risk of reaction was assessed as high and low for foods with the allergen stated as ingredient, or without any mention of allergen. However risk assessment for PAL varied and was judged as higher by non-allergic than allergic participants (82% vs. 58%, p < .001). Understanding of risk associated with PAL also varied by health literacy (p < .001). Both allergic and non-allergic consumers judged all forms of allergy information to be unclear, especially products with no allergy information for non-allergic consumers. Products with a ‘Produced in a Factory’ PAL were perceived as less risky than ’May contain’ or ’Traces of’ PALs (p < .001), less than 40% of participants judged PAL information to be comprehensible, and participants preferred ’May contain’ over the other PALs.Conclusion. Both allergic and non-allergic consumers find allergen information difficult to interpret on packaged foods, and misunderstand PAL, incorrectly distinguishing different risk levels for different PAL wording. Clearer allergy information guidelines are called for, and the use of only one PAL wording is recommended.   Date Accepted: 2021-06-03 Date Submitted: 2021-07-05 Issued: 21-07-2021

This statistic shows a trend in British consumers who find it difficult to understand food labelling in Great Britain. The data is taken from surveys conducted biennially from 2003 to 2017, which asked British adults whether the statement "I often find it difficult to understand the labelling on food" applied to them. Over this period, the share of those finding it difficult to understand food labelling has not changed significantly.

The documentation below is in reference to this items placement in the NM Supply Chain Data Hub. The documentation is of use to understanding the source of this item, and how to reproduce it for updatesTitle: Federal Notice on Food LabelsItem Type: PDFSummary: Food Safety and Inspection Service Labeling Guideline on Documentation Needed To Substantiate Animal Raising Claims for Label Submission, from the USDANotes: Prepared by: Uploaded by EMcRae_NMCDCSource: Linked by Animal Welfare Institute, https://awionline.org/content/consumers-guide-food-labels-and-animal-welfareFeature Service: https://nmcdc.maps.arcgis.com/home/item.html?id=48c876f6a9af45b6b3e9b3494d155787UID: 24Data Requested: Current regulations: who qualifies and who doesnt, who can we help qualify GAP certs, procedure rules, etc.)Method of Acquisition: document downloaded from the Animal Welfare InstituteDate Acquired: 6/23/22Priority rank as Identified in 2022 (scale of 1 being the highest priority, to 11 being the lowest priority): 5Tags: PENDING

The study was designed to determine whether a city-mandated policy requiring calorie labeling at fast food restaurants was associated with consumer awareness of labels, calories purchased, and number of fast food restaurant visits. Point-of-purchase receipts, in-person interviews, and telephone surveys via random-digit dialing were collected as a part of this study on calorie labeling in fast food restaurants. Data was collected in Philadelphia before and after calorie labeling was implemented and in Baltimore, where calorie labeling was not implemented. Baseline collected took place in December 2009 in both Baltimore and Philadelphia. Data was collected after calorie labeling took effect in Philadelphia in February 2010. Further follow-up data collection occurred in June 2010.

Researchers collected data on whether or not consumers reported seeing calorie labeling in the restaurant, whether they bought fewer or more calories as a result of the labeling, and how frequently they went to fast food restaurants. They also collected data on consumer age, gender, race, education, income, and BMI category. A total of 2,083 usable observations across both cities and data collection periods are included in the dataset.

The drug labels and other drug-specific information on this Web site represent the most recent drug listing information companies have submitted to the Food and Drug Administration (FDA). (See 21 CFR part 207.) The drug labeling and other information has been reformatted to make it easier to read but its content has neither been altered nor verified by FDA. The drug labeling on this Web site may not be the labeling on currently distributed products or identical to the labeling that is approved. Most OTC drugs are not reviewed and approved by FDA, however they may be marketed if they comply with applicable regulations and policies described in monographs. Drugs marked 'OTC monograph final' or OTC monograph not final' are not checked for conformance to the monograph. Drugs marked 'unapproved medical gas', 'unapproved homeopathic' or 'unapproved drug other' on this Web site have not been evaluated by FDA for safety and efficacy and their labeling has not been approved. In addition, FDA is not aware of scientific evidence to support homeopathy as effective.

The global frozen food labels market is anticipated to reach a valuation of USD XXX million by 2033, exhibiting a CAGR of XX% during the forecast period (2025-2033). The rising demand for frozen food products due to their convenience, extended shelf life, and cost-effectiveness is a key driving factor behind market growth. Additionally, increasing consumer awareness about the importance of labeling and regulatory compliance is fueling market expansion. The market is segmented by application (foodservice, retail) and type (pressure-sensitive, shrink sleeve, wrap-around). The foodservice segment holds a significant share due to the increasing use of frozen food labels in restaurants, cafes, and catering businesses. Pressure-sensitive labels are the most common type, offering ease of application and versatility for various packaging materials. Key players in the market include LLT Labels, Labelnet, Freezerlabels.net, AstroNova Product Identification, Alpine Packaging, Harfield Components, OnlineLabels, Dot It Nation, Cloud Labels, GA International, UPM Specialty Papers, and Windmill Tapes & Labels. Regional analysis reveals that North America and Europe dominate the market, while Asia Pacific is projected to witness substantial growth due to the increasing demand for frozen food products in emerging economies.

Abstract copyright UK Data Service and data collection copyright owner.

The main aim of this study was to understand quantitatively the views of the general public on the subject of Genetically Modified (GM) food labelling to support a qualitative piece of research. The data were collected using a set of questions on a TNS face-to-face Omnibus Survey.

The objectives of the survey were to:

• understand public awareness and knowledge of GM, e.g. how knowledgeable do the general public claim to be in terms of the use of GM in food or food production and how involved are the general public with GM issues
• understand current use of labelling more generally as well GM labelling, e.g. what information is the general public seeking when purchasing food and is GM information spontaneously suggested?
• understand the general public's response to GM labelling systems (what types of GM do they feel should be labelled and which should not?, what do consumers understand and expect from a product labelled 'GM free'?, what products could be labelled as GM free?)
• understand the claimed impact on the decision to purchase a product if labelled as either as 'GM free' or 'contained GM'
• understand any differences across different segments of the general public

Further information can be found on the Food Standards Agency GM free labelling: exploring public responses to the labelling of GM food and the use of GM-free labelling webpage.


Main Topics:

The survey asked about use of labels more generally when buying food as well as awareness and knowledge of the use of GM. Specific GM food label questions were only asked of those aware of GM. This meant that those unaware of GM were not asked the later questions in the questionnaire. The sample size at these later questions (that is those who were asked specific GM food label questions) was 1,050 interviews.

According to the data, the share of food products with the Italian flag on the label out of a total of about 96,000 food products sold in hyper and supermarkets in Italy in 2023 was 16.1 percent. In 2022, food products with an Italian flag on the label were about 15.9 percent of the total.

The smart food and beverage label market is experiencing robust growth, driven by increasing consumer demand for transparency and traceability in the food supply chain. The rising prevalence of foodborne illnesses and heightened concerns about food safety are key factors fueling this expansion. Furthermore, advancements in technologies such as Radio Frequency Identification (RFID), Near Field Communication (NFC), and temperature sensing labels are enabling sophisticated tracking and monitoring capabilities, enhancing product security and reducing waste. The market's segmentation reflects diverse applications across the food and beverage industry, with RFID and NFC technologies leading in market share due to their advanced data capture and communication capabilities. However, temperature sensing labels are experiencing significant growth, driven by the need for real-time monitoring of perishable goods during transportation and storage. Major players like Avery Dennison, Honeywell, and CCL Industries are actively investing in R&D and strategic partnerships to consolidate their market positions and capitalize on the burgeoning opportunities within this sector. We estimate the market size in 2025 to be $5 billion, with a Compound Annual Growth Rate (CAGR) of 12% projected between 2025 and 2033. The geographical distribution of the smart food and beverage label market reveals strong growth potential in both developed and emerging economies. North America and Europe currently hold substantial market shares, propelled by strong regulatory frameworks and high consumer awareness. However, the Asia-Pacific region is expected to witness the fastest growth over the forecast period, driven by rising disposable incomes, expanding e-commerce penetration, and increasing adoption of advanced technologies in the food and beverage industry. While challenges remain, such as the initial high cost of implementation for some technologies and concerns over data security, the overall market outlook is highly positive. The ongoing trend towards sustainable packaging and the increasing demand for personalized product information further contribute to the sustained growth trajectory of the smart food and beverage label market. The projected market value in 2033 is estimated to be approximately $15 billion based on the estimated CAGR and market dynamics.