10.5281/zenodo.6362952
https://zenodo.org/records/6362952
oai:zenodo.org:6362952
Zinnen, Mathias
Mathias
Zinnen
0000-0003-4366-5216
Friedrich-Alexander-Universität Erlangen-Nürnberg
Madhu, Prathmesh
Prathmesh
Madhu
0000-0003-2707-415X
Friedrich-Alexander-Universität Erlangen-Nürnberg
Kosti, Ronak
Ronak
Kosti
0000-0003-2453-7876
Friedrich-Alexander-Universität Erlangen-Nürnberg
Bell, Peter
Peter
Bell
0000-0003-4415-7408
Philipps-Universität Marburg
Maier, Andreas
Andreas
Maier
0000-0003-2453-7876
Friedrich-Alexander-Universität Erlangen-Nürnberg
Christlein, Vincent
Vincent
Christlein
0000-0003-0455-3799
Friedrich-Alexander-Universität Erlangen-Nürnberg
Odeuropa Dataset of Smell-Related Objects
Zenodo
2022
Object Detection
Smell
Olfaction
Artworks
Odor
Tran, Hang
Hang
Tran
Friedrich-Alexander-Universität Erlangen-Nürnberg
Hussian, Azhar
Azhar
Hussian
Friedrich-Alexander-Universität Erlangen-Nürnberg
Marx, Lizzie
Lizzie
Marx
University of Cambridge
Ehrich, Sofia
Sofia
Ehrich
KNAW Humanities Cluster
Tullett, William
William
Tullett
0000-0002-8975-8031
Anglia Ruskin University Cambridge
2022-03-31
eng
10.5281/zenodo.6362951
https://zenodo.org/communities/odeuropa
https://zenodo.org/communities/eu
1.0.0
Creative Commons Attribution 4.0 International
Odeuropa Dataset of Olfactory Objects
This dataset is released as part of the Odeuropa project. The annotations are identical to the training set of the ICPR2022-ODOR Challenge.
It contains bounding box annotations for smell-active objects in historical artworks gathered from various digital connections.
The smell-active objects annotated in the dataset either carry smells themselves or hint at the presence of smells.
The dataset provides 15823 bounding boxes on 2192 artworks in 87 object categories.
An additional csv file contains further image-level metadata such as artist, collection, or year of creation.
How to use
Due to licensing issues, we cannot provide the images directly, but instead provide a collection of links and a download script.
To get the images, just run the `download_imgs.py` script which loads the images using the links from the `metadata.csv` file. The downloaded images can then be found in the `images` subfolder.
The bounding box annotations can be found in the `annotations.json`. The annotations follow the COCO JSON format, the definition is available here.
The mapping between the `images` array of the `annotations.json` and the `metadata.csv` file can be accomplished via the `file_name` attribute of the elements of the `images` array and the unique `File Name` column of the `metadata.csv` file, respectively.
Additional image-level metadata is available in the `metadata.csv` file.
European Commission
10.13039/501100000780
101004469
ODEUROPA: Negotiating Olfactory and Sensory Experiences in Cultural Heritage Practice and Research