Add pipeline tag and improve model card

README.md

@@ -1,42 +1,47 @@
 ---
-license: mit
 datasets:
 - PaPieta/MozzaVID_Small
 - PaPieta/MozzaVID_Base
 - PaPieta/MozzaVID_Large
+license: mit
+pipeline_tag: image-classification
 tags:
-  - volumetric
-  - 3D
-  - X-ray_tomography
-  - mozzarella
-  - cheese
-  - food_science
+- volumetric
+- 3D
+- X-ray_tomography
+- mozzarella
+- cheese
+- food_science
 ---
 
-# MozzaVID dataset - evaluated models checkpoints
+# MozzaVID: Mozzarella Volumetric Image Dataset
 
-Checkpoints of models evaluated on the MozzaVID dataset in the associated article. 
+This repository contains model checkpoints evaluated on the **MozzaVID** dataset, as presented in the paper "[MozzaVID: Mozzarella Volumetric Image Dataset](https://huggingface.co/papers/2412.04880)".
 
-### [[Paper](https://arxiv.org/abs/2412.04880)] [[Project website](https://papieta.github.io/MozzaVID/)]
+MozzaVID is a large, clean, and versatile volumetric classification dataset containing X-ray computed tomography (CT) images of mozzarella microstructure. It enables the classification of 25 cheese types and 149 cheese samples across three different resolutions.
 
-For details on model use, visit our [GitHub](https://github.com/PaPieta/MozzaVID).
+- **Paper:** [arXiv:2412.04880](https://arxiv.org/abs/2412.04880)
+- **Project Website:** [MozzaVID Project Page](https://papieta.github.io/MozzaVID/)
+- **Repository:** [GitHub - PaPieta/MozzaVID](https://github.com/PaPieta/MozzaVID)
 
 ## Data
 
-Hugging Face - WebDataset format:
-
+The dataset is available on Hugging Face in WebDataset format:
 * [Small split](https://huggingface.co/datasets/PaPieta/MozzaVID_Small)
 * [Base split](https://huggingface.co/datasets/PaPieta/MozzaVID_Base)
 * [Large split](https://huggingface.co/datasets/PaPieta/MozzaVID_Large)
 
-Raw data: [[LINK](https://archive.compute.dtu.dk/files/public/projects/MozzaVID/)].
+Raw data can also be accessed via the [DTU archive](https://archive.compute.dtu.dk/files/public/projects/MozzaVID/).
+
+## Usage
 
+For details on model training and evaluation, please visit the [official GitHub repository](https://github.com/PaPieta/MozzaVID). The repository provides scripts such as `evaluate_model.py` and `train_model.py` to work with these checkpoints.
 
 ## Citation
 
-If you use the dataset or models in your work, please consider citing our publication:
+If you use the dataset or models in your work, please consider citing the following publication:
 
-```
+```bibtex
 @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},
@@ -47,4 +52,4 @@ If you use the dataset or models in your work, please consider citing our public
   primaryClass={cs.CV},
   url={https://arxiv.org/abs/2412.04880},
 }
-```
+```