Bacteria 2033 Images 33 Types Data is a microscopy image dataset for bacterial image classification, transfer learning, and biomedical computer vision research. It contains 2,033 RGB microscopy images representing 33 bacterial types, with images organized by class label.

The dataset is intended for research on microorganism recognition, clinical microscopy image analysis, deep transfer learning, biological digital twins, and related machine learning workflows.

Dataset Details

Field	Description
Dataset type	Image classification
Modality	RGB microscopy images
Number of images	2,033
Number of classes	33 bacterial types
Annotation	Class labels assigned by laboratory experts
Staining	Gram-stained microscopy images
Source material	Clinical sample imagery, including blood, urine, and skin samples
Repository data format	One folder per bacterial class
Download size	Approximately 3.4 GB
License	MIT

Access and Download

The image archive is distributed through Google Drive:

Download the dataset archive

After downloading, extract the archive locally. The expected layout is:

Bacteria-2033Images-33Types-dataset/
|-- class_1/
|   |-- image_001.jpg
|   `-- ...
|-- class_2/
|   |-- image_001.jpg
|   `-- ...
`-- ...

This folder-per-class structure is compatible with common image-classification loaders such as:

PyTorch: torchvision.datasets.ImageFolder
TensorFlow/Keras: tf.keras.utils.image_dataset_from_directory

Intended Uses

This dataset may be used for:

Bacterial species or bacterial type image classification
Deep transfer learning and feature extraction experiments
Microorganism digital twin research
Microscopy image analysis
Biomedical computer vision benchmarking
Federated learning and distributed learning experiments where local image folders are used as client data

Out-of-Scope Uses

This dataset is not intended to be used as a standalone clinical diagnostic system. Models trained on this dataset should not be deployed for clinical decision-making without independent validation, regulatory review, and domain expert oversight.

Recommended Machine Learning Setup

No official train, validation, or test split is provided in the repository. For reproducible research, users should report:

The exact train, validation, and test split strategy
Random seeds
Image resizing and normalization settings
Data augmentation methods
Class balancing or reweighting strategy
Model architecture and pretrained weights, if any

Because 2,033 images are distributed across 33 classes, users should check for class imbalance and consider stratified splitting, augmentation, weighted losses, or balanced sampling.

Data Preprocessing

A typical transfer-learning preprocessing workflow is:

Extract the archive.
Load images with a folder-per-class image loader.
Resize images to the model input size.
Normalize RGB channels using the selected model's expected preprocessing.
Use stratified train, validation, and test splits.

Limitations

The dataset size is moderate for 33-class classification, so models may overfit without augmentation or transfer learning.
Class imbalance may affect evaluation metrics.
Results may depend strongly on image preprocessing, magnification, staining variation, and split strategy.
The dataset should be validated on external microscopy data before use in any applied biomedical setting.

Citation Requirement

If you use this dataset, derivative labels, trained models, figures, benchmarks, or results produced from this dataset, you must cite both of the following papers:

M. B. Jamshidi, D. T. Hoang, D. N. Nguyen, D. Niyato, and M. E. Warkiani, "Revolutionizing biological digital twins: Integrating internet of bio-nano things, convolutional neural networks, and federated learning," Computers in Biology and Medicine, vol. 189, p. 109970, 2025.
M. B. Jamshidi, S. Sargolzaei, S. Foorginezhad, and O. Moztarzadeh, "Metaverse and microorganism digital twins: A deep transfer learning approach," Applied Soft Computing, vol. 147, p. 110798, 2023.

BibTeX

@article{jamshidi2025revolutionizing,
  title={Revolutionizing biological digital twins: Integrating internet of bio-nano things, convolutional neural networks, and federated learning},
  author={Jamshidi, M. B. and Hoang, D. T. and Nguyen, D. N. and Niyato, D. and Warkiani, M. E.},
  journal={Computers in Biology and Medicine},
  volume={189},
  pages={109970},
  year={2025},
  publisher={Elsevier}
}

@article{jamshidi2023metaverse,
  title={Metaverse and microorganism digital twins: A deep transfer learning approach},
  author={Jamshidi, M. B. and Sargolzaei, S. and Foorginezhad, S. and Moztarzadeh, O.},
  journal={Applied Soft Computing},
  volume={147},
  pages={110798},
  year={2023},
  publisher={Elsevier}
}

License

This dataset is released under the MIT License.

Maintainer

Mohammad Behdad Jamshidi

Hugging Face: MBJamshidi
GitHub: MBJamshidi

Downloads last month: 1

Total file size:

3.51 GB