5235536
doi
10.5281/zenodo.5235536
oai:zenodo.org:5235536
Aversa, Marco
Dotphoton AG
Nobis, Gabriel
Fraunhofer HHI
Willis, Kurt
Fraunhofer HHI
Neuenschwander, Yoan
HEPIA/HES-SO
Buck, Michele
Klinikum rechts der Isar
Matek, Christian
Helmholtz Zentrum Munich
Extermann, Jérôme
HEPIA/HES-SO
Pomarico, Enrico
HEPIA/HES-SO
Samek, Wojciech
Fraunhofer HHI
Murray-Smith, Roderick
University of Glasgow
Clausen, Christoph
Dotphoton AG
Sanguinetti, Bruno
Dotphoton AG
Data Models for Dataset Drift Controls in Machine Learning With Optical Images - Datasets
Oala, Luis
Fraunhofer HHI and Dotphoton AG
issn:2835-8856
url:https://openreview.net/forum?id=I4IkGmgFJz
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
raw images
raw data
machine learning
dataset drift
distribution shift
<p>This dataset accompanies the paper titled</p>
<p><em>Data Models for Dataset Drift Controls in Machine Learning with Images</em><br>
<br>
that appeared in the Transactions on Machine Learning Research<br>
<br>
<a href="https://openreview.net/forum?id=I4IkGmgFJz">https://openreview.net/forum?id=I4IkGmgFJz</a><br>
</p>
<pre><code>@article{
oala2023data,
title={Data Models for Dataset Drift Controls in Machine Learning With Optical Images},
author={Luis Oala and Marco Aversa and Gabriel Nobis and Kurt Willis and Yoan Neuenschwander and Mich{\`e}le Buck and Christian Matek and Jerome Extermann and Enrico Pomarico and Wojciech Samek and Roderick Murray-Smith and Christoph Clausen and Bruno Sanguinetti},
journal={Transactions on Machine Learning Research},
issn={2835-8856},
year={2023},
url={https://openreview.net/forum?id=I4IkGmgFJz},
note={}
}</code></pre>
<p>We make available two datasets.</p>
<p><strong>Raw-Microscopy:</strong></p>
<ul>
<li><strong>940 raw bright-field microscopy images</strong> of human blood smear slides for leukocyte classification (microscopy/images/raw_scale100) with corresponding labels (microscopy/labels).</li>
<li><strong>5,640 variations measured at six additional different intensities </strong>(microscopy/images/raw_scale001-raw_scale0075)</li>
<li><strong>11,280 images of the raw sensor data processed through twelve different pipelines</strong> (microscopy/images/processed_views)</li>
</ul>
<p><strong>Raw-Drone:</strong></p>
<ul>
<li><strong>548 raw drone camera images for car segmentation</strong> (drone/images_tiles_256/raw_scale100) with corresponding binary segmentation mask (drone/masks_tiles_256). The images and the masks are cropped from 12 raw drone camera images (drone/images_full/raw_scale100) and 12 masks (drone/masks_full) of size 3648 by 5472.</li>
<li><strong>3,288 variations measured at six additional different intensities</strong> (drone/images_tiles_256/raw_scale001-raw_scale075).</li>
<li><strong>6,576 images of the raw sensor data processed through twelve different pipelines</strong> (drone/images_tiles_256/processed_views).</li>
</ul>
<p>Detailed datasheets for the two datasets can be found in the appendices of the TMLR paper.</p>
<p>The code repository for this project can be found at <a href="https://github.com/aiaudit-org/raw2logit">https://github.com/aiaudit-org/raw2logit</a></p>
<p> </p>
Zenodo
2023-05-02
info:eu-repo/semantics/other
5235535
1.0.0-beta
1683120923.41704
6510039979
md5:6e4eadfffd629dbf66e4b4619bd227bd
https://zenodo.org/records/5235536/files/microscopy.zip
5566051534
md5:5721e41c46f1d2f156ba3c60899ef6c3
https://zenodo.org/records/5235536/files/drone.zip
public
2835-8856
Is published in
issn
https://openreview.net/forum?id=I4IkGmgFJz
Is published in
url
10.5281/zenodo.5235535
isVersionOf
doi
Transactions on Machine Learning Research
2023-05-02