构建了25种马苏里拉奶酪的三维成像数据集，助力体积数据深度学习研究。
MozzaVID: Mozzarella Volumetric Image Dataset
- 基于CT扫描构建多分辨率马苏里拉奶酪三维图像数据集
- 包含149个样本、25种类型，图像数达591至37,824不等
- 适用于食品微观结构分析与通用体积算法开发
受体积成像复杂性影响，现有用于基准测试体积深度学习模型的标准化数据集稀缺，导致新旧模型难以比较，限制了专为体积数据优化的架构发展。为此，我们推出MozzaVID——一个大规模、高质量且多功能的体积分类数据集。该数据集包含马苏里拉奶酪微观结构的X射线计算机断层扫描（CT）图像，支持25种奶酪类型的分类及149个样本的识别。数据提供三种不同分辨率，形成三个实例，图像数量从591到37,824不等。尽管目标是开发通用体积算法，该数据集也支持对马苏里拉奶酪微观结构特性的研究。食品结构的复杂与无序带来挑战，成像方法、尺度和样本量的选择并非易事。本数据集旨在应对这些难题，推动更鲁棒的结构分析模型发展，并深化对食品结构的理解。数据集可访问：https://papieta.github.io/MozzaVID/
原文摘要 · Abstract (English)
Influenced by the complexity of volumetric imaging, there is a shortage of established datasets useful for benchmarking volumetric deep-learning models. As a consequence, new and existing models are not easily comparable, limiting the development of architectures optimized specifically for volumetric data. To counteract this trend, we introduce MozzaVID -- a large, clean, and versatile volumetric classification dataset. Our dataset contains X-ray computed tomography (CT) images of mozzarella microstructure and enables the classification of 25 cheese types and 149 cheese samples. We provide data in three different resolutions, resulting in three dataset instances containing from 591 to 37,824 images. While targeted for developing general-purpose volumetric algorithms, the dataset also facilitates investigating the properties of mozzarella microstructure. The complex and disordered nature of food structures brings a unique challenge, where a choice of appropriate imaging method, scale, and sample size is not trivial. With this dataset, we aim to address these complexities, contributing to more robust structural analysis models and a deeper understanding of food structure. The dataset can be explored through: https://papieta.github.io/MozzaVID/
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