Create README.md

# MozzaVID dataset - Small split

A dataset of synchrotron X-ray tomography scans of mozzarella microstructure, aimed for volumetric model benchmarking and food structure analysis.

### [[Paper](https://arxiv.org/abs/2412.04880)] [[Project website](https://papieta.github.io/MozzaVID/)]

This version is prepared in the webdataset format, optimized for streaming. Check our [GitHub](https://github.com/PaPieta/MozzaVID) for details on how to use it. To download raw data instead, visit: [[LINK](https://archive.compute.dtu.dk/files/public/projects/MozzaVID/)].

## Dataset splits

This is a Small split of the dataset containing 519 volumes. We also provide a [Base split]([TODO](https://huggingface.co/datasets/PaPieta/MozzaVID_Base)) (4728 volumes) and a [Large split](TODO) (37,824 volumes).
<img src="https://cdn-uploads.huggingface.co/production/uploads/67e55a8b793bfd7642b6d84e/Ez67h26Y6-cVUqlpx9mnj.png" alt="dataset_instance_creation.png" width="700"/>

## Citation

If you use the dataset in your work, please consider citing our publication:

```
@misc {pieta2024b,
title={MozzaVID: Mozzarella Volumetric Image Dataset},
author={Pawel Tomasz Pieta and Peter Winkel Rasmussen and Anders Bjorholm Dahl and Jeppe Revall Frisvad and Siavash Arjomand Bigdeli and Carsten Gundlach and Anders Nymark Christensen},
year={2024},
howpublished={arXiv:2412.04880 [cs.CV]},
eprint={2412.04880},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2412.04880},
}
```

## Visual overview

We provide two classification targets/granularities:
* 25 cheese types
* 149 cheese samples

<img src="https://cdn-uploads.huggingface.co/production/uploads/67e55a8b793bfd7642b6d84e/vRtxBSCO6ML6hCpUs0fG5.png" alt="cheese_slices.png" width="1000"/>
Fig 1. Overview of slices from each cheese type, forming the 25 coarse-grained classes.

<img src="https://cdn-uploads.huggingface.co/production/uploads/67e55a8b793bfd7642b6d84e/Y5xq74Z43h4MOlyHH3xPn.png" alt="sample_slices.png" width="1000"/>
Fig 2. Example slices from the fine-grained classes. Each row represents a set of six samples from one cheese type (coarse-grained
class), forming six consecutive fine-grained classes.

README.md

@@ -0,0 +1,16 @@
+---
+license: cc-by-sa-4.0
+task_categories:
+- image-classification
+tags:
+- volumetric
+- 3D
+- X-ray_tomography
+- mozzarella
+- cheese
+- food_science
+size_categories:
+- n<1K
+---
+---
+---