https://github.com/bragasoftware/configilm
A Library for configurable combination of pre-configured and possibly pre-trained Image and Language Models
Science Score: 23.0%
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○Scientific vocabulary similarity
Low similarity (10.5%) to scientific vocabulary
Last synced: 9 months ago
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Repository
A Library for configurable combination of pre-configured and possibly pre-trained Image and Language Models
Basic Info
- Host: GitHub
- Owner: BragaSoftware
- License: mit
- Default Branch: main
- Homepage: https://lhackel-tub.github.io/ConfigILM/
- Size: 231 MB
Statistics
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
- Releases: 0
Fork of lhackel-tub/ConfigILM
Created over 1 year ago
· Last pushed over 1 year ago
https://github.com/BragaSoftware/ConfigILM/blob/main/
#ConfigILM 
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[](https://doi.org/10.1016/j.softx.2024.101731) [](https://github.com/lhackel-tub/ConfigILM/releases) [](https://pypi.org/project/configilm/) [](https://pypi.org/project/configilm/) [](https://opensource.org/licenses/mit-0) [](https://zenodo.org/records/13269357) [](https://github.com/lhackel-tub/ConfigILM/actions/workflows/run_tests.yml) [](https://github.com/lhackel-tub/ConfigILM/actions/workflows/build_docu.yml) [](./coverage.report) [](https://img.shields.io/github/stars/lhackel-tub/ConfigILM?style=social) [](https://github.com/lhackel-tub/ConfigILM/issues) [](https://pypi.org/project/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`](https://pytorch.org/) framework. This **open-source** library provides a convenient implementation for seamlessly combining models from two of the most popular [`pytorch`](https://pytorch.org/) libraries, the highly regarded [`timm`](https://github.com/rwightman/pytorch-image-models) and [`huggingface`](https://huggingface.co/). 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](https://huggingface.co/models), `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`](https://pytorch.org/tutorials/beginner/basics/data_tutorial.html) and [`lightning datamodules`](https://lightning.ai/docs/pytorch/latest/data/datamodule.html), 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 see its [publication](https://doi.org/10.1016/j.softx.2024.101731) and the [documentation](https://lhackel-tub.github.io/ConfigILM). `ConfigILM` is released under the [MIT Software License](https://opensource.org/licenses/mit-0) ## Contributing As an open-source project in a developing field, we are open to contributions. They can be in the form of a new or improved feature or better documentation. For detailed information on how to contribute, see [here](.github/CONTRIBUTING.md). ## Citation If you use this work, please cite ```bibtex @article{hackel2024configilm, title={ConfigILM: A general purpose configurable library for combining image and language models for visual question answering}, author={Hackel, Leonard and Clasen, Kai Norman and Demir, Beg{\"u}m}, journal={SoftwareX}, volume={26}, pages={101731}, year={2024}, publisher={Elsevier} } ``` and the used version of the software, e.g., the current version with ```bibtex @software{lhackel_tub_2024_13269357, author = {lhackel-tub and Kai Norman Clasen}, title = {lhackel-tub/ConfigILM: v0.6.9}, month = aug, year = 2024, publisher = {Zenodo}, version = {v0.6.9}, doi = {10.5281/zenodo.13269357}, url = {https://doi.org/10.5281/zenodo.13269357} } ``` ## 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. Furthermore, we gratefully acknowledge funding from the German Federal Ministry of Education and Research under the grant BIFOLD24B. We also thank [EO-Lab](https://eo-lab.org/en/) for giving us access to their GPUs.
Owner
- Name: Diogo Braga
- Login: BragaSoftware
- Kind: user
- Location: Belo Horizonte, Brazil
- Website: https://linktr.ee/bragasoftware
- Repositories: 1
- Profile: https://github.com/BragaSoftware
Engenheiro de Software.