A state-of-the-art tool for Python developers seeking to rapidly and iteratively develop vision and language models within the [`pytorch`](https://pytorch.org/) framework
Project description
ConfigILM
The library ConfigILM
is a state-of-the-art tool for Python developers seeking to rapidly and
iteratively develop image and language models within the pytorch
framework.
This open-source library provides a convenient implementation for seamlessly combining models
from two of the most popular pytorch
libraries,
the highly regarded timm
and huggingface
🤗.
With an extensive collection of nearly 1000 image and over 100 language models,
with an additional 120,000 community-uploaded models in the huggingface
🤗 model collection,
ConfigILM
offers a diverse range of model combinations that require minimal implementation effort.
Its vast array of models makes it an unparalleled resource for developers seeking to create
innovative and sophisticated image-language models with ease.
Furthermore, ConfigILM
boasts a user-friendly interface that streamlines the exchange of model components,
thus providing endless possibilities for the creation of novel models.
Additionally, the package offers pre-built and throughput-optimized
pytorch dataloaders
and
lightning datamodules
,
which enable developers to seamlessly test their models in diverse application areas, such as Remote Sensing (RS).
Moreover, the comprehensive documentation of ConfigILM
includes installation instructions,
tutorial examples, and a detailed overview of the framework's interface, ensuring a smooth and hassle-free development experience.
For detailed information please visit the publication or the documentation.
ConfigILM
is released under the MIT Software License
Citation
If you use this work, please cite
@software{lhackel_tub_2023,
author = {Leonard Hackel and
Kai Norman Clasen and
Begüm Demir},
title = {lhackel-tub/ConfigILM: v0.4.3},
month = apr,
year = 2023,
publisher = {Zenodo},
version = {v0.4.3},
doi = {10.5281/zenodo.7875406},
url = {https://doi.org/10.5281/zenodo.7875406}
}
Acknowledgement
This work is supported by the European Research Council (ERC) through the ERC-2017-STG BigEarth Project under Grant 759764 and by the European Space Agency through the DA4DTE (Demonstrator precursor Digital Assistant interface for Digital Twin Earth) project and by the German Ministry for Economic Affairs and Climate Action through the AI-Cube Project under Grant 50EE2012B.
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