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ShareScore release 0.9.0
Dataset results
18 results for “food images”
CHOWNET: An Image Dataset of Nigerian Food
<p>CHOWNET-V1 is a high-quality dataset consisting of 118 human-annotated food images, specifically curated for multi-label classification, food object detection, and food captioning tasks. The dataset includes 99 unique labels, serving as a valuable resource for a range of computer vision challenges within the food domain.</p> <p><br>Github Link: <a href="https://github.com/AISaturdaysLagos/chownet">https://github.com/AISaturdaysLagos/chownet</a></p> <p>Data Annotation for CHOWNET-V1 was led by: <a href="https://www.linkedin.com/in/tejumadeafonja/">Tejumade Afonja</a> and <a href="https://www.linkedin.com/in/george-igwegbe/">George Igwegbe</a></p> <p>This dataset was contributed by the AI Saturdays Lagos community in 2018.</p> <p> </p> <blockquote> <p>The dataset structure is described in About.txt</p> </blockquote>
NutriGreen Image Dataset: A Collection of Annotated Nutrition, Organic, and Vegan Food Products
<p>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.</p> <p>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.</p> <p> </p> <p> </p>
Quantitative sodium MRI in foods: addressing sensitivity issues using single quantum Chemical Shift Imaging at high field
<p>Quantitative sodium MRI in foods: addressing sensitivity issues using single quantum Chemical Shift Imaging at high field</p>
The influence of social presence on facial affective responses to food images (FSC)
<p>Raw data from a study of social context and food liking. CSV files are generated by PsychoPy. ACQ files are generated by BIOPAC Acqknowledge software.</p> <p>Modified versions of Acqknowledge files have had markers for chocolate consumption readjusted by the researcher. These participants had failed to click the mouse in time with the start and finish of their eating episode. 188modified.acq file had the marker channel for the first trial edited because the fixation data for the first trial was missing.</p> <p> </p>
Free Living Food Waste Management and Diet Quality Improvement Using Smart Intervention and Food Image Application
ClinicalTrials.gov study NCT05061888. IPD Sharing: Not stated. Countries: 1. Publications: 0.
High Throughput Multispectral Image Processing with applications in Food Science
<p>Raw image samples for the PLoS ONE paper entitled "High Throughput Multispectral Image Processing with applications in Food Science".</p> <p>Segmented images for the PLoS ONE paper entitled "High Throughput Multispectral Image Processing with applications in Food Science".</p>
Image Dataset for 'AI-enabled biosensing for rapid pathogen detection: from liquid food to agricultural water'
<p>This dataset is presented in the following publication. Please cite this publication if you use the dataset.</p> <p><em>Jiyoon Yi, Nicharee Wisuthiphaet, Pranav Raja, Nitin Nitin, J. Mason Earles. (2023). AI-enabled biosensing for rapid pathogen detection: from liquid food to agricultural water. Water Research, 120258. doi: <a href="https://doi.org/10.1016/j.watres.2023.120258">10.1016/j.watres.2023.120258</a></em></p>
Food Effect Study For Apixaban Commercial Image Tablets
ClinicalTrials.gov study NCT01437839. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Influence of a Medicinal Cannabinoid Agonist on Responses to Food Images and Food Intake
ClinicalTrials.gov study NCT02310347. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Pilot Testing of Food Images in Children
ClinicalTrials.gov study NCT03338634. IPD Sharing: NO. Countries: 1. Publications: 3.
Food Quantification Using a Novel, User Friendly Plate Imaging System
ClinicalTrials.gov study NCT07183449. IPD Sharing: NO. Countries: 1. Publications: 4.
Comparing Neural Responses to Food Images in EDNOS Patients and Healthy Controls Using fMRI
ClinicalTrials.gov study NCT01882023. IPD Sharing: Not stated. Countries: 1. Publications: 15.
Monitoring food structure during digestion using small-angle scattering and imaging techniques
<p>Monitoring food structure during digestion using small-angle scattering and imaging techniques</p>
A Study to Characterize Event Related Potential Markers of Attentional Bias Towards Words and Images of Food
ClinicalTrials.gov study NCT01366508. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Exploration of the Mechanisms of Vulnerability of Anorexia Nervosa at an Early Age : Study of the Cognitive Treatment of Food Stimuli and Body Image
ClinicalTrials.gov study NCT04037215. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Physiological Response and Visual Attention to Visual Food Images in Healthy Subjects and in Functional Dyspepsia Patients
ClinicalTrials.gov study NCT02727556. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Effects of Racial Congruence, "Likes", and Food Images in Social Media Ads on Adolescents' Caloric Intake - Study 3
ClinicalTrials.gov study NCT06969651. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Functional Magnetic Resonance Imaging (fMRI) Exploration of Neurocognitive Processes Involved in Food Addiction (FA) in Obese Patients: Towards New Phenotypic Markers for an Optimized Care Pathway
ClinicalTrials.gov study NCT03454711. IPD Sharing: NO. Countries: 1. Publications: 0.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.