| tags: | |
| - medical-imaging | |
| - segmentation | |
| - nnu-net | |
| - airway | |
| - ct | |
| # CaliTree weights | |
| These are the trained weights for CaliTree, a solution to the ATM'26 (MICCAI 2026) airway tree modelling challenge. | |
| Code: **[adinathdukre/CaliTree](https://github.com/adinathdukre/CaliTree)** | |
| | folder | model | use | | |
| |---|---|---| | |
| | `track1/nnUNet_ckpts` | nnU-Net ResEnc-M, `nnUNetTrainerAirwayCB_clprec025`, 5 folds | Track 1 binary airway segmentation | | |
| | `track2/nnUNet_ckpts` | nnU-Net ResEnc-M, `nnUNetTrainerAirwayCB`, 5 folds | Track 2 stage 1 airway mask | | |
| | `track2/nnUNet_t2warm` | nnU-Net `3d_fullres_SAS`, `nnUNetTrainerT2Warm`, 5 folds | Track 2 stage 2 21-class voxel labelling | | |
| Each track folder is used directly as `ATM_RESOURCES`: | |
| ```bash | |
| huggingface-cli download adidukre/CaliTree --local-dir /path/to/weights | |
| ATM_RESOURCES=/path/to/weights/track1 ATM_INPUT=/path/to/ct_folder ATM_OUTPUT=/path/to/output \ | |
| python docker/T1/inference.py | |
| ``` | |