We present 'CongNaMul', a comprehensive dataset designed for various tasks in soybean sprouts image analysis. The CongNaMul dataset is curated to facilitate tasks such as image classification, semantic segmentation, decomposition, and measurement of length and weight. The classification task provides four classes to determine the quality of soybean sprouts: normal, broken, spotted, and broken and spotted, for the development of AI-aided automatic quality inspection technology. For semantic segmentation, images with varying complexity, from single sprout images to images with multiple sprouts, along with human-labelled mask images, are included. The label has 4 different classes: background, head, body, tail. The dataset also provides images and masks for the image decomposition task, including two separate sprout images and their combined form. Lastly, 5 physical features of sprouts (head length, body length, body thickness, tail length, weight) are provided for image-based measurement tasks. This dataset is expected to be a valuable resource for a wide range of research and applications in the advanced analysis of images of soybean sprouts. Also, we hope that this dataset can assist researchers studying classification, semantic segmentation, decomposition, and physical feature measurement in other industrial fields, in evaluating their models. The dataset is available at the authors' repository. (https://bhban.kr/data)

翻译：我们提出了"CongNaMul"——一个专为豆芽图像分析中多种任务设计的综合性数据集。该数据集旨在支持图像分类、语义分割、分解以及长度和重量测量等任务。其中，分类任务提供了四个类别（正常、破损、斑点、破损且斑点）以判定豆芽品质，用于开发AI辅助的自动质量检测技术。在语义分割方面，数据集包含了从单芽图像到多芽图像等不同复杂度的图像，以及人工标注的掩膜图像。标签包含四个不同类别：背景、头部、主体、尾部。该数据集还提供了用于图像分解任务的图像和掩膜，包括两株分离的豆芽图像及其组合形式。最后，数据集提供了豆芽的5项物理特征（头长、体长、体厚、尾长、重量），用于基于图像的测量任务。该数据集预计将成为豆芽图像高级分析领域广泛研究和应用的重要资源。同时，我们希望该数据集能够帮助其他工业领域中从事分类、语义分割、分解及物理特征测量研究的学者评估其模型。数据集可通过作者仓库获取。(https://bhban.kr/data)