Image Segmentation
MLX
English
native-mlx
apple-silicon
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 plans.json from MedMLX/siam: direct link, hf CLI and curl.
- Browser
- Download file 11.2 kB
-
https://huggingface.co/MedMLX/siam/resolve/main/plans.json
- Command line
-
hf download hf://MedMLX/siam/plans.json
-
curl -L -o plans.json https://huggingface.co/MedMLX/siam/resolve/main/plans.json
11.2 kB
| { | |
| "dataset_name": "Dataset1007_Partialization_MixLowDill", | |
| "plans_name": "nnUNetResEncUNetXLPlans", | |
| "original_median_spacing_after_transp": [ | |
| 0.75, | |
| 0.75, | |
| 0.75 | |
| ], | |
| "original_median_shape_after_transp": [ | |
| 332, | |
| 331, | |
| 259 | |
| ], | |
| "image_reader_writer": "SimpleITKIO", | |
| "transpose_forward": [ | |
| 0, | |
| 1, | |
| 2 | |
| ], | |
| "transpose_backward": [ | |
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| "configurations": { | |
| "2d": { | |
| "data_identifier": "nnUNetPlans_2d", | |
| "preprocessor_name": "DefaultPreprocessor", | |
| "batch_size": 107, | |
| "patch_size": [ | |
| 384, | |
| 320 | |
| ], | |
| "median_image_size_in_voxels": [ | |
| 331.0, | |
| 259.0 | |
| ], | |
| "spacing": [ | |
| 0.75, | |
| 0.75 | |
| ], | |
| "normalization_schemes": [ | |
| "ZScoreNormalization" | |
| ], | |
| "use_mask_for_norm": [ | |
| false | |
| ], | |
| "resampling_fn_data": "resample_data_or_seg_to_shape", | |
| "resampling_fn_seg": "resample_data_or_seg_to_shape", | |
| "resampling_fn_data_kwargs": { | |
| "is_seg": false, | |
| "order": 3, | |
| "order_z": 0, | |
| "force_separate_z": null | |
| }, | |
| "resampling_fn_seg_kwargs": { | |
| "is_seg": true, | |
| "order": 1, | |
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| "force_separate_z": null | |
| }, | |
| "resampling_fn_probabilities": "resample_data_or_seg_to_shape", | |
| "resampling_fn_probabilities_kwargs": { | |
| "is_seg": false, | |
| "order": 1, | |
| "order_z": 0, | |
| "force_separate_z": null | |
| }, | |
| "architecture": { | |
| "network_class_name": "dynamic_network_architectures.architectures.unet.ResidualEncoderUNet", | |
| "arch_kwargs": { | |
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| "conv_op": "torch.nn.modules.conv.Conv2d", | |
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| 1 | |
| ], | |
| "conv_bias": true, | |
| "norm_op": "torch.nn.modules.instancenorm.InstanceNorm2d", | |
| "norm_op_kwargs": { | |
| "eps": 1e-05, | |
| "affine": true | |
| }, | |
| "dropout_op": null, | |
| "dropout_op_kwargs": null, | |
| "nonlin": "torch.nn.LeakyReLU", | |
| "nonlin_kwargs": { | |
| "inplace": true | |
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| }, | |
| "_kw_requires_import": [ | |
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| "dropout_op", | |
| "nonlin" | |
| ] | |
| }, | |
| "batch_dice": true | |
| }, | |
| "3d_fullres": { | |
| "data_identifier": "nnUNetPlans_3d_fullres", | |
| "preprocessor_name": "DefaultPreprocessor", | |
| "batch_size": 2, | |
| "patch_size": [ | |
| 256, | |
| 256, | |
| 192 | |
| ], | |
| "median_image_size_in_voxels": [ | |
| 332.0, | |
| 331.0, | |
| 259.0 | |
| ], | |
| "spacing": [ | |
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| 0.75, | |
| 0.75 | |
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| ], | |
| "use_mask_for_norm": [ | |
| false | |
| ], | |
| "resampling_fn_data": "resample_data_or_seg_to_shape", | |
| "resampling_fn_seg": "resample_data_or_seg_to_shape", | |
| "resampling_fn_data_kwargs": { | |
| "is_seg": false, | |
| "order": 3, | |
| "order_z": 0, | |
| "force_separate_z": null | |
| }, | |
| "resampling_fn_seg_kwargs": { | |
| "is_seg": true, | |
| "order": 1, | |
| "order_z": 0, | |
| "force_separate_z": null | |
| }, | |
| "resampling_fn_probabilities": "resample_data_or_seg_to_shape", | |
| "resampling_fn_probabilities_kwargs": { | |
| "is_seg": false, | |
| "order": 1, | |
| "order_z": 0, | |
| "force_separate_z": null | |
| }, | |
| "architecture": { | |
| "network_class_name": "dynamic_network_architectures.architectures.unet.ResidualEncoderUNet", | |
| "arch_kwargs": { | |
| "n_stages": 7, | |
| "features_per_stage": [ | |
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| 64, | |
| 128, | |
| 256, | |
| 320, | |
| 320, | |
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| "n_blocks_per_stage": [ | |
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| 6, | |
| 6, | |
| 6 | |
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| 1, | |
| 1 | |
| ], | |
| "conv_bias": true, | |
| "norm_op": "torch.nn.modules.instancenorm.InstanceNorm3d", | |
| "norm_op_kwargs": { | |
| "eps": 1e-05, | |
| "affine": true | |
| }, | |
| "dropout_op": null, | |
| "dropout_op_kwargs": null, | |
| "nonlin": "torch.nn.LeakyReLU", | |
| "nonlin_kwargs": { | |
| "inplace": true | |
| } | |
| }, | |
| "_kw_requires_import": [ | |
| "conv_op", | |
| "norm_op", | |
| "dropout_op", | |
| "nonlin" | |
| ] | |
| }, | |
| "batch_dice": false | |
| } | |
| }, | |
| "experiment_planner_used": "nnUNetPlannerResEncXL", | |
| "label_manager": "LabelManager", | |
| "foreground_intensity_properties_per_channel": { | |
| "0": { | |
| "max": 9.146286010742188, | |
| "mean": 0.3542199730873108, | |
| "median": 0.2677832394838333, | |
| "min": -0.3361438810825348, | |
| "percentile_00_5": -0.05218610167503357, | |
| "percentile_99_5": 1.8381230425834687, | |
| "std": 0.33286988735198975 | |
| } | |
| } | |
| } |