NutriGreen Image Dataset: Annotating Nutrition, Organic, and Vegan Labels for Food Product Classification

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.

本研究提出NutriGreen数据集，该数据集收录了各类预包装食品的图像样本。数据集中的每张图像均附带三类独立标注标签：其一为基于营养评分体系（Nutri-Score）的营养价值标签，其二为标注食品是否为纯素或素食的素食标识（V-label），其三为展示欧盟有机认证（BIO）的标识。该数据集共计收录10472张图像，其中Nutri-Score标签分为五个子类别：A类1250张、B类1107张、C类867张、D类1001张、E类967张。此外，带有V标识的图像共870张，带有BIO标识的图像共2328张，无标签图像共3201张。为验证该标注数据集的应用价值，我们对YOLOv5模型进行了微调，最终取得了94.0%的优异准确率。上述亮眼结果表明，本数据集在训练食品标识检测相关的创新系统方面具备显著应用潜力，同时亦可作为新兴计算机视觉系统的优质基准数据集。

搜集汇总

背景与挑战

背景概述

NutriGreen图像数据集是一个包含10,472张包装食品图像的数据集，专门用于食品标签分类，每张图像标注了Nutri-Score营养评分（分为A到E五个等级）、V-label素食/纯素食标签和欧盟有机认证（BIO）标志。该数据集规模较大，标签分布多样，包括3,201张无标签图像，已通过微调YOLOv5模型达到94.0%准确率，验证了其在训练计算机视觉系统检测食品标签方面的实用性和潜力。