NutritionVerse-Real: An Open Access Manually Collected 2D Food Scene Dataset for Dietary Intake Estimation

• URL: http://arxiv.org/abs/2401.08598v1
• Date: Mon, 20 Nov 2023 11:05:20 GMT
• Title: NutritionVerse-Real: An Open Access Manually Collected 2D Food Scene Dataset for Dietary Intake Estimation
• Authors: Chi-en Amy Tai, Saeejith Nair, Olivia Markham, Matthew Keller, Yifan Wu, Yuhao Chen, Alexander Wong
• Abstract summary: We introduce NutritionVerse-Real, an open access manually collected 2D food scene dataset for dietary intake estimation. The NutritionVerse-Real dataset was created by manually collecting images of food scenes in real life, measuring the weight of every ingredient and computing the associated dietary content of each dish.
• Score: 68.49526750115429
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Dietary intake estimation plays a crucial role in understanding the nutritional habits of individuals and populations, aiding in the prevention and management of diet-related health issues. Accurate estimation requires comprehensive datasets of food scenes, including images, segmentation masks, and accompanying dietary intake metadata. In this paper, we introduce NutritionVerse-Real, an open access manually collected 2D food scene dataset for dietary intake estimation with 889 images of 251 distinct dishes and 45 unique food types. The NutritionVerse-Real dataset was created by manually collecting images of food scenes in real life, measuring the weight of every ingredient and computing the associated dietary content of each dish using the ingredient weights and nutritional information from the food packaging or the Canada Nutrient File. Segmentation masks were then generated through human labelling of the images. We provide further analysis on the data diversity to highlight potential biases when using this data to develop models for dietary intake estimation. NutritionVerse-Real is publicly available at https://www.kaggle.com/datasets/nutritionverse/nutritionverse-real as part of an open initiative to accelerate machine learning for dietary sensing.

Related papers

• NutritionVerse-Direct: Exploring Deep Neural Networks for Multitask Nutrition Prediction from Food Images [63.314702537010355]
  Self-reporting methods are often inaccurate and suffer from substantial bias. Recent work has explored using computer vision prediction systems to predict nutritional information from food images. This paper aims to enhance the efficacy of dietary intake estimation by leveraging various neural network architectures.
  arXiv  Detail & Related papers  (2024-05-13T14:56:55Z)
• How Much You Ate? Food Portion Estimation on Spoons [63.611551981684244]
  Current image-based food portion estimation algorithms assume that users take images of their meals one or two times. We introduce an innovative solution that utilizes stationary user-facing cameras to track food items on utensils. The system is reliable for estimation of nutritional content of liquid-solid heterogeneous mixtures such as soups and stews.
  arXiv  Detail & Related papers  (2024-05-12T00:16:02Z)
• NutritionVerse: Empirical Study of Various Dietary Intake Estimation Approaches [59.38343165508926]
  Accurate dietary intake estimation is critical for informing policies and programs to support healthy eating. Recent work has focused on using computer vision and machine learning to automatically estimate dietary intake from food images. We introduce NutritionVerse- Synth, the first large-scale dataset of 84,984 synthetic 2D food images with associated dietary information. We also collect a real image dataset, NutritionVerse-Real, containing 889 images of 251 dishes to evaluate realism.
  arXiv  Detail & Related papers  (2023-09-14T13:29:41Z)
• UMDFood: Vision-language models boost food composition compilation [26.5694236976957]
  We propose a novel vision-language model, UMDFood-VL, using front-of-package labeling and product images to accurately estimate food composition profiles. Up to 82.2% of selected products' estimated error between chemical analysis results and model estimation results are less than 10%. This performance sheds light on generalization towards other food and nutrition-related data compilation and catalyzation.
  arXiv  Detail & Related papers  (2023-05-18T03:18:12Z)
• NutritionVerse-3D: A 3D Food Model Dataset for Nutritional Intake Estimation [65.47310907481042]
  One in four older adults are malnourished. Machine learning and computer vision show promise of automated nutrition tracking methods of food. NutritionVerse-3D is a large-scale high-resolution dataset of 105 3D food models.
  arXiv  Detail & Related papers  (2023-04-12T05:27:30Z)
• Towards the Creation of a Nutrition and Food Group Based Image Database [58.429385707376554]
  We propose a framework to create a nutrition and food group based image database. We design a protocol for linking food group based food codes in the U.S. Department of Agriculture's (USDA) Food and Nutrient Database for Dietary Studies (FNDDS) Our proposed method is used to build a nutrition and food group based image database including 16,114 food datasets.
  arXiv  Detail & Related papers  (2022-06-05T02:41:44Z)
• Nutrition5k: Towards Automatic Nutritional Understanding of Generic Food [8.597152169571057]
  We introduce Nutrition5k, a novel dataset of 5k diverse, real world food dishes with corresponding video streams, depth images, component weights, and high accuracy nutritional content annotation. We demonstrate the potential of this dataset by training a computer vision algorithm capable of predicting the caloric and macronutrient values of a complex, real world dish at an accuracy that outperforms professional nutritionists.
  arXiv  Detail & Related papers  (2021-03-04T22:59:22Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.