TINTOlib - Python library for transforming tabular data into synthetic images

Name: TINTOlib
Author: Manuel Castillo-Cara

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We’ve just launched a 100% free course on Udemy about using TINTOlib and developing Hybrid Neural Networks.
Learn how to turn tabular data into synthetic images and apply CNNs, ViTs, and hybrid architectures like a pro.

👉 Access the Course on Udemy

🧠 Overview

TINTOlib is a powerful Python library that transforms tidy tabular data into synthetic images, enabling the use of deep learning models like CNNs and Vision Transformers (ViTs). This bridges the gap between structured and image-based data for tasks such as classification and regression.

📺 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.

🔧 Features

Input formats: CSV or Pandas DataFrame
Designed for tidy data (target column last)
Output: grayscale images from reduction and transformation methods
Compatible with Linux, Windows, macOS
Requires Python 3.7+

🧪 Supported Models

Supported image transformation models include:

Models	Class	Hyperparameters
TINTO	`TINTO()`	`problem` `normalize` `verbose` `pixels` `algorithm` `blur` `submatrix` `amplification` `distance` `steps` `option` `times` `train_m` `zoom` `random_seed`
IGTD	`IGTD()`	`problem` `normalize` `verbose` `scale` `fea_dist_method` `image_dist_method` `error` `max_step` `val_step` `switch_t` `min_gain` `zoom` `random_seed`
REFINED	`REFINED()`	`problem` `normalize` `verbose` `hcIterations` `n_processors` `zoom` `random_seed`
BarGraph	`BarGraph()`	`problem` `normalize` `verbose` `pixel_width` `gap` `zoom`
DistanceMatrix	`DistanceMatrix()`	`problem` `normalize` `verbose` `zoom`
Combination	`Combination()`	`problem` `normalize` `verbose` `zoom`
SuperTML	`SuperTML()`	`problem` `normalize` `verbose` `pixels` `feature_importance` `font_size` `random_seed`
FeatureWrap	`FeatureWrap()`	`problem` `normalize` `verbose` `size` `bins` `zoom`
BIE	`BIE()`	`problem` `normalize` `verbose` `precision` `zoom`

Each model has its own hyperparameters and behaviors. See documentation for usage.

🚀 Getting Started

Install via pip:

pip install TINTOlib

To run examples:

Use requirements.txt for the base environment
Use requirements-example.txt for full deep learning workflows

🧩 Example Code

    from TINTOlib.tinto import TINTO
    model = TINTO(problem="supervised, "blur=True)
    model.fit_transform(data, folder)

💬 Learn More

📚 Cite TINTOlib

If you use TINTOlib, please cite the following papers:

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}}

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},
volume={22},
pages={101391},
year = {2023},
issn = {2352-7110},
doi = {10.1016/j.softx.2023.101391}
}

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 = {10.1016/j.inffus.2022.10.011}
}

🎓 License

TINTOlib is released under the Apache License 2.0.

Ph.D. Manuel Castillo-Cara