SNAPMe: A Benchmark Dataset of Food Photos with Food Records for Evaluation of Computer Vision Algorithms in the Context of Dietary Assessment

Photo-based dietary assessment methods are becoming more feasible as artificial intelligence methods improve. However, advancement of these methods to the level usable in nutrition studies has been hindered by the lack of a dataset against which to benchmark algorithm performance. We conducted the Surveying Nutrient Assessment with Photographs of Meals (SNAPMe) Study (ClinicalTrials ID: NCT05008653 ) to develop a benchmark dataset of food photographs paired with traditional food records. Participants were recruited nationally and completed enrollment meetings via web-based video conferencing. By the end of the study, 90 participants had completed all three days of data collection; 95 participants completed at least one study day. Participants uploaded and annotated their meal photos using a mobile phone app called Bitesnap and completed food records using the Automated Self-Administered 24-hour Dietary Assessment Tool (ASA24®) on the same day. A sizing marker with black and white boxes of known size were included in meal photos. Participants included photos “before” and “after” eating non-packaged and multi-serving packaged meals, as well as photos of the “front” package label and “ingredient” label for single-serving packaged foods. In total, the SNAPMe Database (DB) contains 3,311 unique food photos linked with 275 ASA24 food records from 95 participants who photographed all foods consumed and recorded food records in parallel for up to 3 study days each for a total of 275 diet days. The SNAPMe DB includes 1,475 “before” photos of non-packaged foods, 1,436 “after” photos of non-packaged foods, 203 “front” photos of packaged foods, and 196 “ingredient” labels of packaged foods. Each line item of each ASA24 food record is linked to the relevant photo. These data will be transformative for the improvement of artificial intelligence algorithms for the adoption of photo-based dietary assessment in nutrition research. Resources in this dataset:Resource Title: snapme_db_09Dec2022.tar.gz. File Name: snapme_db_09Dec2022.tar.gzResource Description: This is a gzipped tarball. To expand and read the README,
gunzip snapme_db_09Dec2022.tar.gz; tar -xvf snapme_db_09Dec2022.tar
cd snapme_db_09Dec2022
less README.txt

随着人工智能方法的不断进步，基于照片的饮食评估方法正日益可行。然而，由于缺乏用于评估算法性能的基准数据集，这些方法在达到可用于营养研究水平的进展受到了阻碍。本研究开展了名为“通过照片进行营养评估调查”（SNAPMe）的研究（临床试验ID：NCT05008653），旨在开发一套包含食物照片与传统食物记录配对的基准数据集。参与者在全国范围内招募，并通过基于网络的视频会议完成注册会议。截至研究结束，90名参与者完成了所有三天的数据收集；95名参与者完成了至少一天的研究。参与者使用名为Bitesnap的移动应用程序上传并标注他们的餐食照片，并在同一天使用自动自我管理24小时膳食评估工具（ASA24®）完成食物记录。餐食照片中包含有黑白方框且尺寸已知的尺寸标记。参与者包括未包装和分装包装餐食的“前后”照片，以及单份包装食品的“正面”包装标签和“成分”标签。总而言之，SNAPMe数据库（DB）包含3,311张独特的食物照片，与95名参与者的275条ASA24食物记录相链接。这些参与者拍摄了所摄入的所有食物的照片，并在最多3天的研究日内并行记录了食物记录，总计275个饮食日。SNAPMe数据库包括1,475张非包装食品的“前”照片，1,436张非包装食品的“后”照片，203张包装食品的“正面”照片，以及196张包装食品的“成分”标签。每条ASA24食物记录的每一项都与相关的照片相链接。这些数据将为改善人工智能算法在营养研究中的基于照片的饮食评估采纳提供革命性的影响。数据集中包含的资源：资源标题：snapme_db_09Dec2022.tar.gz。文件名：snapme_db_09Dec2022.tar.gz。资源描述：这是一个gzip压缩的tar包。要展开和读取README，请执行以下命令：
gunzip snapme_db_09Dec2022.tar.gz; tar -xvf snapme_db_09Dec2022.tar
cd snapme_db_09Dec2022
less README.txt