TINTO - Transform Tabular Data into Synthetic Images for Deep Neural Netowoks

Abstract

TINTO is an open-source, user-extendable framework that offers new opportunities for users to convert tidy data into images through the representation of characteristic pixels. For this transformation, TINTO implemented two-dimensional reduction algorithms, such as PCA and t-SNE. Our proposal also includes a technique used in painting known as blurring, which adds more ordered information to the image and can improve the classification task in CNNs.

📺 VideoTutorial Course (English/Spanish)

🎥 Prefer not to register on Udemy or looking for the English version of the course? No worries — you can follow the full course directly on GitHub!

This hands-on tutorial includes bilingual videos (English/Spanish) and practical notebooks to help you learn how to use TINTOlib with deep learning models like CNNs, ViTs, and hybrid architectures.

📄 Documentation

You can find all the documentation and sources of TINTO in OEG GitHub.

Video Example

🔍 Main Features

• 📄 Works with CSV files in Tidy Data format
• 🧪 Input: All numeric data; target variable in the last column
• 🔧 Supports two projection methods: PCA and t-SNE
• 🖼️ Output: Black-and-white synthetic images
• 🌀 Blurring technique for pixel blending
• 🐍 Python 3.7+, compatible with Linux, Windows, macOS

📥 Input

The following table shows a classic example of the IRIS CSV dataset as it should look like for the run:

–

📥 Output

The following Figure show the output of TINTO:

📖 Citation

If you used TINTOlib with Hybrid Neural Networks in your work, please cite the IEEE Journal of Selected Topics in Signal Processing:

@ARTICLE{10946146,
  author={Castillo-Cara, Manuel and Martínez-Gómez, Jesus and Ballesteros-Jerez, Javier and García-Varea, Ismael and García-Castro, Raúl and Orozco-Barbosa, Luis},
  journal={IEEE Journal of Selected Topics in Signal Processing}, 
  title={MIMO-Based Indoor Localisation with Hybrid Neural Networks: Leveraging Synthetic Images from Tidy Data for Enhanced Deep Learning}, 
  year={2025},
  volume={},
  number={},
  pages={1-13},
  keywords={Location awareness;Accuracy;Neural networks;Measurement;Deep learning;Complexity theory;Antennas;Antenna measurements;Base stations;Signal processing algorithms;Massive MIMO;Deep Learning;Hybrid Neural Network;Synthetic Images;Positioning;Indoor Localisation},
  doi={10.1109/JSTSP.2025.3555067}}

If you used TINTO in your work, please cite the INFFUS Paper:

@article{inffus_TINTO,
    title = {A novel deep learning approach using blurring image techniques for Bluetooth-based indoor localisation},
    journal = {Information Fusion},
    author = {Reewos Talla-Chumpitaz and Manuel Castillo-Cara and Luis Orozco-Barbosa and Raúl García-Castro},
    volume = {91},
    pages = {173-186},
    year = {2023},
    issn = {1566-2535},
    doi = {https://doi.org/10.1016/j.inffus.2022.10.011}
}

And the SoftwareX paper

@article{softwarex_TINTO,
    title = {TINTO: Converting Tidy Data into Image for Classification with 2-Dimensional Convolutional Neural Networks},
    journal = {SoftwareX},
    author = {Manuel Castillo-Cara and Reewos Talla-Chumpitaz and Raúl García-Castro and Luis Orozco-Barbosa},
    year = {2023},
    issn = {2352-7110},
    volume = {22},
    pages = {101391},
    doi = {https://doi.org/10.1016/j.softx.2023.1013911}
}

License & Links

• 📦 Code: GitHub Repository
• 📄 License: Apache 2.0