vesselFM_base.pt
: VesselFM pre-trained on our three proposed data sources (D_real, D_drand, and D_flow). This checkpoint will be automatically downloaded in
vesselfm/seg/inference.py
.
Citing vesselFM
If you find our work useful, please cite:
@article{wittmann2024vesselfm,
title={vesselFM: A Foundation Model for Universal 3D Blood Vessel Segmentation},
author={Wittmann, Bastian and Wattenberg, Yannick and Amiranashvili, Tamaz and Shit, Suprosanna and Menze, Bjoern},
journal={arXiv preprint arXiv:2411.17386},
year={2024}
}
Runs of bwittmann vesselFM on huggingface.co
997
Total runs
14
24-hour runs
118
3-day runs
202
7-day runs
-4.0K
30-day runs
More Information About vesselFM huggingface.co Model
vesselFM huggingface.co is an AI model on huggingface.co that provides vesselFM's model effect (), which can be used instantly with this bwittmann vesselFM model. huggingface.co supports a free trial of the vesselFM model, and also provides paid use of the vesselFM. Support call vesselFM model through api, including Node.js, Python, http.
vesselFM huggingface.co is an online trial and call api platform, which integrates vesselFM's modeling effects, including api services, and provides a free online trial of vesselFM, you can try vesselFM online for free by clicking the link below.
bwittmann vesselFM online free url in huggingface.co:
vesselFM is an open source model from GitHub that offers a free installation service, and any user can find vesselFM on GitHub to install. At the same time, huggingface.co provides the effect of vesselFM install, users can directly use vesselFM installed effect in huggingface.co for debugging and trial. It also supports api for free installation.