Data Models for Dataset Drift Controls in Machine Learning With Optical Images - Datasets

This dataset accompanies the paper titled <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> https://openreview.net/forum?id=I4IkGmgFJz<br> <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> We make available two datasets. <strong>Raw-Microscopy:</strong> <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). <strong>5,640 variations measured at six additional different intensities </strong>(microscopy/images/raw_scale001-raw_scale0075) <strong>11,280 images of the raw sensor data processed through twelve different pipelines</strong> (microscopy/images/processed_views) <strong>Raw-Drone:</strong> <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. <strong>3,288 variations measured at six additional different intensities</strong> (drone/images_tiles_256/raw_scale001-raw_scale075). <strong>6,576 images of the raw sensor data processed through twelve different pipelines</strong> (drone/images_tiles_256/processed_views). Detailed datasheets for the two datasets can be found in the appendices of the TMLR paper. The code repository for this project can be found at https://github.com/aiaudit-org/raw2logit