Image Segmentation
MLX
English
native-mlx
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medical-imaging
mri
ct
brain
head
segmentation
nnunet
research
Instructions to use MedMLX/siam with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use MedMLX/siam with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] hf download MedMLX/siam --local-dir siam
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
Download NOTICE from MedMLX/siam: direct link, hf CLI and curl.
- Browser
- Download file 752 Bytes
-
https://huggingface.co/MedMLX/siam/resolve/main/NOTICE
- Command line
-
hf download hf://MedMLX/siam/NOTICE
-
curl -L -o NOTICE https://huggingface.co/MedMLX/siam/resolve/main/NOTICE
752 Bytes
| SIAM: Segment It All Model, v0.3 | |
| Original authors: R. Valabregue, I. Khemir, E. Bardinet, F. Rousseau, G. Auzias, R. Dorent | |
| Original repository: https://github.com/romainVala/SIAM (Apache-2.0) | |
| Original weights: https://zenodo.org/records/17733431 (pred_DS108_LcsfP_Ano, CC-BY-4.0) | |
| Paper: https://arxiv.org/abs/2605.02737 | |
| The weights are licensed under CC-BY-4.0 (https://creativecommons.org/licenses/by/4.0/). | |
| Credit the original authors and cite the paper above. | |
| Changes: the PyTorch nnU-Net checkpoints were converted to float32 MLX safetensors | |
| for nnunet-mlx. Optimizer state and duplicate decoder aliases of encoder weights were | |
| not carried over. No retraining, pruning or quantization. | |
| MLX conversion by Joseph Sandoval and Alfredo Sandoval Jr. | |