A Python library that transforms tidy tabular data into synthetic images so you can use computer vision models (CNNs, Vision Transformers) on non-image data.

Which methods are supported?

TINTO, IGTD, REFINED, BarGraph, DistanceMatrix, Combination, SuperTML, FeatureWrap, BIE — each with specific hyperparameters.

Why convert tables into images?

It lets you leverage the advances of modern vision models on tabular problems, sometimes improving accuracy and generalization.

TINTOlib - Python library for transforming tabular data into synthetic images

Name: TINTOlib
Author: Manuel Castillo-Cara

TINTOlib — Tabular Data → Synthetic Images

Open-source Python library to convert tidy tabular data into synthetic images and train CNNs, ViTs, and hybrid architectures.

⭐ GitHub 📘 Documentation

🎉 New Free Course on Udemy! 🎉

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.

👉 Access the Course on Udemy

🧠 Overview

TINTOlib transforms tidy tabular data into synthetic images, enabling deep learning with CNNs and Vision Transformers (ViTs) for classification and regression. It bridges structured data and image-based learning, and supports hybrid models combining both worlds.

📦 pip install TINTOlib 🖥️ Linux / Windows / macOS 🐍 Python 3.7+ 🧪 CSV / Pandas DataFrame

🎬 TINTOlib — Overview Video

📺 VideoTutorial Course (English/Spanish)

Prefer not to register on Udemy or looking for the English version? Follow the full bilingual course on GitHub, with videos and practical notebooks covering CNNs, ViTs, and hybrid architectures.

📊 Benchmark of TINTOlib vs Classical Models

This benchmark compares TINTOlib transformation methods and neural architectures against traditional and ensemble models on multiple datasets. You can explore the interactive version directly on the official site.

🌐 Visit TINTOlib Benchmark Page

🔧 Features

Input formats: CSV or Pandas DataFrame.
Designed for tidy data (target column at the end).
Output: grayscale images produced by reduction/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`

🚀 Getting Started

Install via pip:

pip install TINTOlib

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

🧩 Example

from TINTOlib.tinto import TINTO

# Create and run TINTO on your tidy DataFrame (target column last)
model = TINTO(problem="supervised", blur=True, pixels=64, random_seed=42)
model.fit_transform(data, folder="outputs")

📚 Additional Resources

📘 Documentation 🚀 GitHub 📹 Crash Course

🧠 Research and Software Publications

📄 Research Articles

Manuel Castillo-Cara et al. MIMO-Based Indoor Localisation with Hybrid Neural Networks. IEEE JSTSP. DOI: 10.1109/JSTSP.2025.3555067
Reewos Talla-Chumpitaz, Manuel Castillo-Cara et al. Blurring Image Techniques for Bluetooth-based Indoor Localisation. Information Fusion. DOI: 10.1016/j.inffus.2022.10.011

💾 Software Articles

Manuel Castillo-Cara et al. TINTO: Converting Tidy Data into Images. SoftwareX. DOI: 10.1016/j.softx.2023.101391

Ph.D. Manuel Castillo-Cara