Improve Hugging Face dataset card

README.md

@@ -1,57 +1,144 @@
-# Bacteria-2033Images-33Types-dataset
+---
+license: mit
+pretty_name: Bacteria 2033 Images 33 Types Data
+task_categories:
+- image-classification
+language:
+- en
+tags:
+- bacteria
+- microscopy
+- microbiology
+- biomedical
+- medical-imaging
+- gram-stain
+- computer-vision
+- image-classification
+- transfer-learning
+- digital-twin
+size_categories:
+- 1K<n<10K
+viewer: false
+---
 
-Microscopy image dataset of 33 bacteria species for machine learning and deep learning research.
+# Bacteria 2033 Images 33 Types Data
 
-## 🧫 33-Class Bacterial Microscopy Image Dataset (2033 Images)
+## Dataset Summary
 
-This dataset contains 2,033 high-resolution RGB images of **33 bacterial species**, collected from clinical samples (blood, urine, skin), Gram-stained, and annotated by laboratory experts.
+**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.
 
-![bacteria](https://github.com/user-attachments/assets/7a3044b8-8f1d-427b-8e32-169414c42652)
+The dataset is intended for research on microorganism recognition, clinical microscopy image analysis, deep transfer learning, biological digital twins, and related machine learning workflows.
 
----
+![Bacteria microscopy examples](https://github.com/user-attachments/assets/7a3044b8-8f1d-427b-8e32-169414c42652)
 
-## Dataset Facts
+## Dataset Details
 
-| Property | Value |
+| Field | Description |
 |---|---|
-| Total images | 2,033 |
-| Number of classes | 33 bacterial species |
-| Image type | High-resolution RGB |
-| Sample source | Clinical (blood, urine, skin) |
-| Staining method | Gram stain |
-| Annotation | Expert laboratory annotation |
-| Download size | ~3.4 GB (ZIP) |
+| 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
 
-## 📦 Download the Dataset
+The image archive is distributed through Google Drive:
 
-🔗 [Click here to download the dataset (3.4 GB, Google Drive)](https://drive.google.com/file/d/1aR7Dz11wKV3t7awnnnO32UE_37MYF6wX/view?usp=sharing)
+[Download the dataset archive](https://drive.google.com/file/d/1aR7Dz11wKV3t7awnnnO32UE_37MYF6wX/view?usp=sharing)
 
----
+After downloading, extract the archive locally. The expected layout is:
 
-## 📚 Key Publications
+```text
+Bacteria-2033Images-33Types-dataset/
+|-- class_1/
+|   |-- image_001.jpg
+|   `-- ...
+|-- class_2/
+|   |-- image_001.jpg
+|   `-- ...
+`-- ...
+```
 
-1. Jamshidi, M. B., Sargolzaei, S., Foorginezhad, S., & Moztarzadeh, O. (2023). Metaverse and microorganism digital twins: A deep transfer learning approach. *Applied Soft Computing*, 147, 110798.
-   https://www.sciencedirect.com/science/article/pii/S1568494623008165
+This folder-per-class structure is compatible with common image-classification loaders such as:
 
-2. Jamshidi, M.B., Hoang, D.T., Nguyen, D.N., Niyato, D. and Warkiani, M.E. (2025). Revolutionizing biological digital twins: Integrating internet of bio-nano things, convolutional neural networks, and federated learning. *Computers in Biology and Medicine*, 189, 109970.
-   https://www.sciencedirect.com/science/article/pii/S001048252500321X
+- PyTorch: `torchvision.datasets.ImageFolder`
+- TensorFlow/Keras: `tf.keras.utils.image_dataset_from_directory`
 
-3. Jamshidi, M., Hoang, D.T. and Nguyen, D.N. (2024). CNN-FL for Biotechnology Industry Empowered by Internet-of-BioNano Things and Digital Twins. *IEEE Internet of Things Magazine*, 7(5), 54–63.
-   https://ieeexplore.ieee.org/abstract/document/10643983
+## 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:
+
+1. Extract the archive.
+2. Load images with a folder-per-class image loader.
+3. Resize images to the model input size.
+4. Normalize RGB channels using the selected model's expected preprocessing.
+5. 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:
 
-## 📥 Citation
+1. 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.
 
-If you use this dataset in any research paper, project, book, thesis, or any other scholarly or commercial work, please cite it as follows:
+2. 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
 
 ```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, Mohammad Behdad and Sargolzaei, Saleh and Foorginezhad, Salimeh and Moztarzadeh, Omid},
+  author={Jamshidi, M. B. and Sargolzaei, S. and Foorginezhad, S. and Moztarzadeh, O.},
   journal={Applied Soft Computing},
   volume={147},
   pages={110798},
@@ -60,40 +147,13 @@ If you use this dataset in any research paper, project, book, thesis, or any oth
 }
 ```
 
----
-
-## 🤖 Working with the Dataset (ML/AI)
+## License
 
-- **Download**: Download the ZIP from Google Drive (link above) and extract locally. There is no programmatic API.
-- **Directory layout**: One folder per class label (33 folders), each containing the class images.
-- **Recommended frameworks**: PyTorch (`torchvision.datasets.ImageFolder`) or TensorFlow/Keras (`image_dataset_from_directory`) both work naturally with this per-class folder layout.
-- **Class imbalance**: 2,033 images across 33 classes averages ~62 images per class. Expect imbalance; use weighted loss or data augmentation accordingly.
-- **Preprocessing**: Standard ImageNet normalization is a reasonable starting point for transfer learning on these Gram-stained RGB images.
-- **Train/val/test splits**: No official split is defined. Stratified splitting to preserve class proportions is recommended, consistent with published papers.
-
----
-
-## 🗂️ Repository Structure
-
-```
-Bacteria-2033Images-33Types-dataset/
-├── README.md       # Dataset description, download link, citation info
-└── LICENSE         # MIT License
-```
-
-The image data (3.4 GB) is distributed via Google Drive, not stored in this repository.
-
----
-
-## 🛠️ Development Workflow (for Contributors)
-
-- Work on a feature branch, never directly on `main`.
-- All changes in this repository are documentation-only (no code or binary data).
-- Do not commit image data or large binary files.
-- Push the feature branch and open a pull request into `main` when ready.
-
----
+This dataset is released under the MIT License.
 
 ## Maintainer
 
-Mohammad Behdad Jamshidi ([@MBJamshidi](https://github.com/MBJamshidi))
+Mohammad Behdad Jamshidi
+
+- Hugging Face: [MBJamshidi](https://huggingface.co/MBJamshidi)
+- GitHub: [MBJamshidi](https://github.com/MBJamshidi)