TINTOlib — From Tabular Data to Synthetic Images for Deep Learning
TINTOlib — Tabular Data → Synthetic Images
Open-source Python library that transforms tabular data into synthetic images, enabling the use of vision-based models such as CNNs, ViTs, and hybrid architectures.
🎉 Learn TINTOlib for Free
📺 GitHub
Bilingual video tutorials + notebooks.
🌍 Talk Map: TINTOlib Around the World
Key conferences and seminars where TINTOlib has been presented or applied.
🧠 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
🧪 Supported Methods in TINTOlib
| Method | Description |
|---|---|
| TINTO | Converts tidy tabular data into synthetic images using several layout algorithms and optional image preprocessing. |
| IGTD | Places features on a grid based on correlations, producing synthetic images that preserve variable relationships. |
| REFINED | Optimizes the 2D placement of variables to enhance feature locality for CNN processing. |
| BarGraph | Represents each sample as a bar-chart image of its feature values for direct CNN classification. |
| DistanceMatrix | Builds an image from pairwise feature distances, revealing similarity patterns in a matrix form. |
| Combination | Fuses multiple image encodings into a single input, merging complementary representations. |
| SuperTML | Transforms tabular data into text-based images, embedding values directly as text. |
| FeatureWrap | Encodes features in circular/spiral patterns to emphasize variable ordering and grouping. |
| BIE | Generates compact, binary-encoded images from tabular features for classification. |
🏛️ Partner Institutions
