ocbe-uio/imml
A Python package for integrating, processing, and analyzing incomplete multi-modal datasets.
Integrating different types of data, like images, text, and sensor readings, can be challenging, especially when some information is missing. This project helps researchers and data scientists combine these diverse, incomplete datasets, process them, and then perform analyses like finding hidden patterns or grouping similar items. It takes your raw, multi-modal datasets (even with gaps) and outputs insights, classifications, or transformed data ready for further study.
Available on PyPI.
Use this if you need to analyze datasets that combine multiple types of information, such as medical records with patient images, lab results, and genetic data, and you're struggling with missing pieces of information across these different data sources.
Not ideal if your data is perfectly complete and tidy across all modalities, as its primary strength is handling missing values in multi-modal contexts.
Stars
24
Forks
2
Language
Python
License
BSD-3-Clause
Category
Last pushed
Mar 11, 2026
Commits (30d)
0
Dependencies
6
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/ocbe-uio/imml"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
sktime/skpro
A unified framework for tabular probabilistic regression, time-to-event prediction, and...
WenjieDu/Awesome_Imputation
Awesome Deep Learning for Time-Series Imputation, including an unmissable paper and tool list...
WenjieDu/PyGrinder
PyGrinder: a Python toolkit for grinding data beans into the incomplete for real-world data...
DoubleML/doubleml-for-r
DoubleML - Double Machine Learning in R
MIDASverse/rMIDAS
R package for missing-data imputation with deep learning