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

This dataset accompanies the paper titled

Data Models for Dataset Drift Controls in Machine Learning with Images

that appeared in the Transactions on Machine Learning Research

https://openreview.net/forum?id=I4IkGmgFJz
 

@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={}
}

We make available two datasets.

Raw-Microscopy:

• 940 raw bright-field microscopy images of human blood smear slides for leukocyte classification (microscopy/images/raw_scale100) with corresponding labels (microscopy/labels).
• 5,640 variations measured at six additional different intensities (microscopy/images/raw_scale001-raw_scale0075)
• 11,280 images of the raw sensor data processed through twelve different pipelines (microscopy/images/processed_views)

Raw-Drone:

• 548 raw drone camera images for car segmentation (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.
• 3,288 variations measured at six additional different intensities (drone/images_tiles_256/raw_scale001-raw_scale075).
• 6,576 images of the raw sensor data processed through twelve different pipelines (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