DIBaS Dataset

Dataset details

Last updated: 15 Dec 2022
Meta Album ID MCR.BCT
Domain ID MCR
Domain Name Microscopic
Set Number 0
Dataset ID BCT
Dataset Name DIBaS
Short Description Digital Image of Bacterial Species (DIBaS)
Long Description The Digital Images of Bacteria Species dataset (DIBaS) (https://github.com/gallardorafael/DIBaS-Dataset) is a dataset of 33 bacterial species with around 20 images for each species. For the Meta-Album, since the images were large (2 048x1 532) with very few samples in each class, we decided to split each image into several smaller images before resizing them to 128x128. We then obtained a preprocessed dataset of 4 060 images with at least 108 images for each class. This dataset was also preprocessed with blob normalization techniques, which is quite unusual for this type of image. The goal of this transformation was to reduce the importance of color in decision-making for a bias-aware challenge.
# Classes 33
# Images 4060
Keywords microscopic, bacteria
Data Format images
Image size 128x128
License
(original data release)		 Public for researchers
License URL
(original data release)
License
(Meta-Album data release)		 CC BY-NC 4.0
License URL
(Meta-Album data release)		 https://creativecommons.org/licenses/by-nc/4.0/
Source Digital Image of Bacterial Species (DIBaS)
Source URL http://misztal.edu.pl/software/databases/dibas/
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0184554
https://github.com/gallardorafael/DIBaS-Dataset
Original Author Bartosz Zielinski, Anna Plichta, Krzysztof Misztal, Przemyslaw Spurek, Monika Brzychczy-Wloch, Dorota Ochonska
Original contact krzysztof.misztal@uj.edu.pl
Meta Album author Romain Mussard
Created Date 01 March 2022
Contact Name Ihsan Ullah
Contact Email meta-album@chalearn.org
Contact URL https://meta-album.github.io/

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Dataset Info Datasheet

Download Dataset from OpenML

Dataset Version OpenML ID
Micro 44237 Download
Mini 44281 Download
Extended 44316 Download

Code to download dataset using OpenML API

      # import openml
      import openml
  
      # download dataset with DATASET_ID. DATASET_ID is OpenML ID
      dataset = openml.datasets.get_dataset(DATASET_ID)
  
      # display dataset info
      print(dataset.name)

Sample Images

Cite this dataset 

@article{10.1371/journal.pone.0184554,
    doi = {10.1371/journal.pone.0184554},
    author = {Zielinski, Bartosz AND Plichta, Anna AND Misztal, Krzysztof AND Spurek, Przemyslaw AND Brzychczy-Wloch, Monika AND Ochonska, Dorota},
    journal = {PLOS ONE},
    publisher = {Public Library of Science},
    title = {Deep learning approach to bacterial colony classification},
    year = {2017},
    month = {09},
    volume = {12},
    url = {https://doi.org/10.1371/journal.pone.0184554},
    pages = {1-14},
    number = {9}
}
Download as bib

Cite Meta-Album

  @inproceedings{meta-album-2022,
    title={Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification},
    author={Ullah, Ihsan and Carrion, Dustin and Escalera, Sergio and Guyon, Isabelle M and Huisman, Mike and Mohr, Felix and van Rijn, Jan N and Sun, Haozhe and Vanschoren, Joaquin and Vu, Phan Anh},
    booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track},
    url = {https://meta-album.github.io/},
    year = {2022}
  }
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