Downloads · 30 days
0
tal-grossman/SymmAdapt
SymmAdapt is a image segmentation model from tal-grossman. Use it for the image segmentation task on the model card, and read the license before you ship it in a product. It is set up for pytorch. The card lists the license as mit.
Weights for SymmAdapt: source-free domain adaptation of a SymmFlow (SymmFMSD) segmenter.
Downloads · 30 days
0
Access
Public
Updated Sep 6, 2026
Repo size
6.9 GB
Likes
0
Public
Click a slice to open those files.
.pt6.9 GB · 100%
From the Hugging Face model README
Weights for SymmAdapt: source-free domain adaptation of a SymmFlow (SymmFMSD) segmenter.
Code: github.com/tal-grossman/SymmAdapt_public
These files are the paper MRI→CT run (CHAOS MRI → BTCV CT, four organs: spleen, kidneys, liver).
| File | What it is |
|---|---|
source_mri_best.pt | Source model trained on labeled MRI (Phase I) |
gst_mri_to_ct_best.pt | After generative self-training on unlabeled CT (Phase III) |
Load them with the public repo (bash scripts/download_checkpoints.sh, then scripts/run_eval.sh). Do not expect a Hugging Face from_pretrained API — they are PyTorch trainer checkpoints (ema_state_dict).
Not included here:
LatFM_coco_beta6.0.pt)stabilityai/sd-vae-ft-mse (pulled at runtime)MIT
@misc{grossman2026symmadaptsymmetricalflowmatching,
title={SymmAdapt: Symmetrical Flow Matching for Source-Free Domain Adaptation in Medical Image Segmentation},
author={Tal Grossman and Noa Cahan and Hayit Greenspan},
year={2026},
eprint={2608.22532},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2608.22532},
}
Also cite SymmFlow if you use these weights:
@article{caetano2025symmetrical,
title = {Symmetrical Flow Matching: Unified Image Generation, Segmentation, and Classification with Score-Based Generative Models},
author = {Caetano, Francisco and Viviers, Christiaan and De With, Peter HN and van der Sommen, Fons},
journal = {arXiv preprint arXiv:2506.10634},
year = {2025}
}