MozzaVID: Mozzarella Volumetric Image Dataset
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 volumetri...
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Zusammenfassung: | 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 being general-purpose, the dataset also
facilitates investigating mozzarella structure properties. The structure of
food directly affects its functional properties and thus its consumption
experience. Understanding food structure helps tune the production and
mimicking it enables sustainable alternatives to animal-derived food products.
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. The dataset can be downloaded from:
https://archive.compute.dtu.dk/files/public/projects/MozzaVID/. |
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DOI: | 10.48550/arxiv.2412.04880 |