pybioclip 2.1.5

Python package that simplifies using the BioCLIP foundation model.

pybioclip

---

Command line tool and python package to simplify using BioCLIP (current and earlier versions), including for taxonomic or other label prediction on (and thus annotation or labeling of) images, as well as for generating semantic embeddings for images. No particular understanding of ML or computer vision is required to use it. It also implements a number of performance optimizations for batches of images or custom class lists, which should be particularly useful for integration into computational workflows.

Documentation

See the pybioclip documentation website for requirements, installation instructions, and tutorials.

License

pybioclip is distributed under the terms of the MIT license.

Citation

To cite this repository, please use the citation provided by Cite this repository in the sidebar, which uses the information in the CITATON.cff file. If you need a citation with a version-specific DOI, you can obtain this by following the Zenodo DOI badge at the top of this file.

Unless you selected (via --model) a model different from the default (which is now BioCLIP 2), please also cite the BioCLIP 2 paper:

@inproceedings{gu2025bioclip,
  title={Bio{CLIP} 2: Emergent Properties from Scaling Hierarchical Contrastive Learning},
  author={Jianyang Gu and Samuel Stevens and Elizabeth G Campolongo and Matthew J Thompson and Net Zhang and Jiaman Wu and Andrei Kopanev and Zheda Mai and Alexander E. White and James Balhoff and Wasila Dahdul and Daniel Rubenstein and Hilmar Lapp and Tanya Berger-Wolf and Wei-Lun Chao and Yu Su},
  booktitle={The Thirty-ninth Annual Conference on Neural Information Processing Systems},
  year={2025},
  url={https://openreview.net/forum?id=yPC9zmkQgG}
}

If you selected the original BioCLIP model (using --model), please cite the original BioCLIP paper:

@inproceedings{stevens2024bioclip,
  title = {{B}io{CLIP}: A Vision Foundation Model for the Tree of Life}, 
  author = {Samuel Stevens and Jiaman Wu and Matthew J Thompson and Elizabeth G Campolongo and Chan Hee Song and David Edward Carlyn and Li Dong and Wasila M Dahdul and Charles Stewart and Tanya Berger-Wolf and Wei-Lun Chao and Yu Su},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024}
}

Also consider citing the BioCLIP code:

@software{bioclip2023code,
  author = {Samuel Stevens and Jiaman Wu and Matthew J. Thompson and Elizabeth G. Campolongo and Chan Hee Song and David Edward Carlyn},
  doi = {10.5281/zenodo.10895871},
  title = {BioCLIP},
  version = {v1.0.0},
  year = {2024}
}