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We present 'CongNaMul', a comprehensive dataset designed for various tasks insoybean sprouts image analysis. The CongNaMul dataset is curated to facilitatetasks such as image classification, semantic segmentation, decomposition, andmeasurement of length and weight. The classification task provides four classesto determine the quality of soybean sprouts: normal, broken, spotted, andbroken and spotted, for the development of AI-aided automatic qualityinspection technology. For semantic segmentation, images with varyingcomplexity, from single sprout images to images with multiple sprouts, alongwith human-labelled mask images, are included. The label has 4 differentclasses: background, head, body, tail. The dataset also provides images andmasks for the image decomposition task, including two separate sprout imagesand their combined form. Lastly, 5 physical features of sprouts (head length,body length, body thickness, tail length, weight) are provided for image-basedmeasurement tasks. This dataset is expected to be a valuable resource for awide range of research and applications in the advanced analysis of images ofsoybean sprouts. Also, we hope that this dataset can assist researchersstudying classification, semantic segmentation, decomposition, and physicalfeature measurement in other industrial fields, in evaluating their models. Thedataset is available at the authors' repository. (https://bhban.kr/data)

CongNaMul: A Dataset for Advanced Image Processing of Soybean Sprouts