Instructions to use MedMLX/totalsegmentator-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use MedMLX/totalsegmentator-mlx with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] hf download MedMLX/totalsegmentator-mlx --local-dir totalsegmentator-mlx
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
Download 300/plans.json from MedMLX/totalsegmentator-mlx: direct link, hf CLI and curl.
- Browser
- Download file 6.87 kB
-
https://huggingface.co/MedMLX/totalsegmentator-mlx/resolve/main/300/plans.json
- Command line
-
hf download hf://MedMLX/totalsegmentator-mlx/300/plans.json
-
curl -L -o plans.json https://huggingface.co/MedMLX/totalsegmentator-mlx/resolve/main/300/plans.json
6.87 kB
| { | |
| "dataset_name": "Dataset300_body_6mm_1559subj", | |
| "plans_name": "nnUNetPlans", | |
| "original_median_spacing_after_transp": [ | |
| 6.0, | |
| 6.0, | |
| 6.0 | |
| ], | |
| "original_median_shape_after_transp": [ | |
| 57, | |
| 57, | |
| 60 | |
| ], | |
| "image_reader_writer": "NibabelIOWithReorient", | |
| "transpose_forward": [ | |
| 0, | |
| 1, | |
| 2 | |
| ], | |
| "transpose_backward": [ | |
| 0, | |
| 1, | |
| 2 | |
| ], | |
| "configurations": { | |
| "2d": { | |
| "data_identifier": "nnUNetPlans_2d", | |
| "preprocessor_name": "DefaultPreprocessor", | |
| "batch_size": 811, | |
| "patch_size": [ | |
| 64, | |
| 64 | |
| ], | |
| "median_image_size_in_voxels": [ | |
| 57.0, | |
| 60.0 | |
| ], | |
| "spacing": [ | |
| 6.0, | |
| 6.0 | |
| ], | |
| "normalization_schemes": [ | |
| "CTNormalization" | |
| ], | |
| "use_mask_for_norm": [ | |
| false | |
| ], | |
| "UNet_class_name": "PlainConvUNet", | |
| "UNet_base_num_features": 32, | |
| "n_conv_per_stage_encoder": [ | |
| 2, | |
| 2, | |
| 2, | |
| 2, | |
| 2 | |
| ], | |
| "n_conv_per_stage_decoder": [ | |
| 2, | |
| 2, | |
| 2, | |
| 2 | |
| ], | |
| "num_pool_per_axis": [ | |
| 4, | |
| 4 | |
| ], | |
| "pool_op_kernel_sizes": [ | |
| [ | |
| 1, | |
| 1 | |
| ], | |
| [ | |
| 2, | |
| 2 | |
| ], | |
| [ | |
| 2, | |
| 2 | |
| ], | |
| [ | |
| 2, | |
| 2 | |
| ], | |
| [ | |
| 2, | |
| 2 | |
| ] | |
| ], | |
| "conv_kernel_sizes": [ | |
| [ | |
| 3, | |
| 3 | |
| ], | |
| [ | |
| 3, | |
| 3 | |
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| [ | |
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| 3 | |
| ], | |
| [ | |
| 3, | |
| 3 | |
| ], | |
| [ | |
| 3, | |
| 3 | |
| ] | |
| ], | |
| "unet_max_num_features": 512, | |
| "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 | |
| }, | |
| "batch_dice": true | |
| }, | |
| "3d_fullres": { | |
| "data_identifier": "nnUNetPlans_3d_fullres", | |
| "preprocessor_name": "DefaultPreprocessor", | |
| "batch_size": 20, | |
| "patch_size": [ | |
| 64, | |
| 64, | |
| 64 | |
| ], | |
| "median_image_size_in_voxels": [ | |
| 57.0, | |
| 57.0, | |
| 60.0 | |
| ], | |
| "spacing": [ | |
| 6.0, | |
| 6.0, | |
| 6.0 | |
| ], | |
| "normalization_schemes": [ | |
| "CTNormalization" | |
| ], | |
| "use_mask_for_norm": [ | |
| false | |
| ], | |
| "UNet_class_name": "PlainConvUNet", | |
| "UNet_base_num_features": 32, | |
| "n_conv_per_stage_encoder": [ | |
| 2, | |
| 2, | |
| 2, | |
| 2, | |
| 2 | |
| ], | |
| "n_conv_per_stage_decoder": [ | |
| 2, | |
| 2, | |
| 2, | |
| 2 | |
| ], | |
| "num_pool_per_axis": [ | |
| 4, | |
| 4, | |
| 4 | |
| ], | |
| "pool_op_kernel_sizes": [ | |
| [ | |
| 1, | |
| 1, | |
| 1 | |
| ], | |
| [ | |
| 2, | |
| 2, | |
| 2 | |
| ], | |
| [ | |
| 2, | |
| 2, | |
| 2 | |
| ], | |
| [ | |
| 2, | |
| 2, | |
| 2 | |
| ], | |
| [ | |
| 2, | |
| 2, | |
| 2 | |
| ] | |
| ], | |
| "conv_kernel_sizes": [ | |
| [ | |
| 3, | |
| 3, | |
| 3 | |
| ], | |
| [ | |
| 3, | |
| 3, | |
| 3 | |
| ], | |
| [ | |
| 3, | |
| 3, | |
| 3 | |
| ], | |
| [ | |
| 3, | |
| 3, | |
| 3 | |
| ], | |
| [ | |
| 3, | |
| 3, | |
| 3 | |
| ] | |
| ], | |
| "unet_max_num_features": 320, | |
| "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 | |
| }, | |
| "batch_dice": false | |
| } | |
| }, | |
| "experiment_planner_used": "ExperimentPlanner", | |
| "label_manager": "LabelManager", | |
| "foreground_intensity_properties_per_channel": { | |
| "0": { | |
| "max": 31554.0, | |
| "mean": -43.95634460449219, | |
| "median": 0.0, | |
| "min": -42354.0, | |
| "percentile_00_5": -989.0, | |
| "percentile_99_5": 1383.0, | |
| "std": 371.595703125 | |
| } | |
| } | |
| } |