| --- |
| license: other |
| license_name: slimformer-plusplus-noncommercial-research-1.0 |
| license_link: LICENSE |
| pipeline_tag: image-segmentation |
| tags: |
| - pytorch |
| - nnunet |
| - medical-imaging |
| - 3d-segmentation |
| - slimformer-plusplus |
| - mamba |
| - arxiv:2610.25488 |
| --- |
| |
| # SlimFormer++ — ACDC |
|
|
| Official fold-0 pretrained checkpoint for **3D MRI medical image |
| segmentation**, released with |
| [SlimFormer++: Token Mixing Where It Matters for Efficient 3D Medical Image Segmentation](https://arxiv.org/abs/2610.25488). |
|
|
| [Source code](https://github.com/WHHHHHY822/SlimFormerPlusPlus) · [Paper](https://arxiv.org/abs/2610.25488) · |
| [Hugging Face Papers](https://huggingface.co/papers/2610.25488) · |
| [Original checkpoint release](https://github.com/WHHHHHY822/SlimFormerPlusPlus/releases/tag/acdc-best-v1) · |
| [Dataset](https://www.creatis.insa-lyon.fr/Challenge/acdc/) |
|
|
| This repository mirrors the original inference-only checkpoint **without |
| changing the selected network weights**, along with its nnU-Net plans and |
| dataset metadata. Training images, segmentation masks, and optimizer state |
| are not distributed here. |
|
|
| ## Model |
|
|
| SlimFormer++ is a custom nnU-Net v2 trainer. It uses windowed Mamba mixing in |
| the first encoder stage, residual channel MLPs in deeper stages, and a |
| depthwise-convolution bottleneck augmented with sparse long-range context. |
| This checkpoint is for the original Mamba-based model. The separate Mamba-free |
| implementation has no released pretrained checkpoint. |
|
|
| | Setting | Value | |
| | --- | --- | |
| | Dataset | ACDC (`Dataset003_ACDC`) | |
| | Input | One MRI channel, NIfTI `.nii.gz` | |
| | Trainer | `nnUNetTrainer_SlimFormerPlusPlus` | |
| | Configuration / fold | `3d_fullres` / `0` | |
| | Selected checkpoint epoch | 254 | |
| | Completed training epochs | 1200 | |
| | Training / validation cases | 240 / 60 | |
| | Inference patch | `[20, 256, 224]` | |
|
|
| ### Output labels |
|
|
| | Label ID | Structure | |
| | --- | --- | |
| | 0 | background | |
| | 1 | right ventricle | |
| | 2 | myocardium | |
| | 3 | left ventricle | |
|
|
| ## Evaluation |
|
|
| The paper's Table III results and the released checkpoint's fold-0 validation |
| measurements are reported separately. They are different evaluation records. |
| The fold-0 results are **not independent test-set results**. |
|
|
| | Evaluation record | Mean Dice (%) | Mean HD95 (mm) | |
| | --- | ---: | ---: | |
| | Paper, Table III | 91.56 | 3.70 | |
| | Released checkpoint, fold-0 validation (60 cases) | 91.5969 | 3.3040 | |
|
|
| The validation aggregates are copied from the project's published |
| [reproduction materials](https://github.com/WHHHHHY822/SlimFormerPlusPlus/blob/92e66fcd8428bd9aeae94e44df36842078c60006/experiments/acdc/metrics.json). |
| The [protocol](reproduction/protocol.json) records training and inference |
| settings; [metrics](reproduction/metrics.json) contains the reference |
| aggregates. See the [reproduction guide](https://github.com/WHHHHHY822/SlimFormerPlusPlus/blob/92e66fcd8428bd9aeae94e44df36842078c60006/experiments/README.md) |
| for data preparation, original splits, per-case results and evaluation commands. |
|
|
| ## Use the checkpoint |
|
|
| 1. Follow the project's [installation guide](https://github.com/WHHHHHY822/SlimFormerPlusPlus/blob/92e66fcd8428bd9aeae94e44df36842078c60006/docs/INSTALLATION.md). |
| The reference setup uses Linux, an NVIDIA GPU, Python 3.12, PyTorch 2.6.0 |
| and CUDA Toolkit 12.6. Mamba and causal-conv1d are compiled from source. |
| Source revision used for this model card: `92e66fcd8428bd9aeae94e44df36842078c60006`. |
| 2. Install `huggingface_hub` in that environment and download this repository |
| into a fresh nnU-Net results directory: |
|
|
| ```bash |
| python -m pip install huggingface_hub |
| export nnUNet_results=/path/to/empty/nnUNet_results |
| hf download Yang0822/SlimFormerPlusPlus-ACDC --local-dir "$nnUNet_results" |
| (cd "$nnUNet_results" && sha256sum -c SHA256SUMS) |
| ``` |
|
|
| 3. Prepare one input volume per case as `CASE_ID_0000.nii.gz`, using the |
| matching MRI modality, and run: |
|
|
| ```bash |
| export nnUNet_compile=false |
| nnUNetv2_predict -i /path/to/imagesTs -o /path/to/predictions \ |
| -d 3 -c 3d_fullres \ |
| -tr nnUNetTrainer_SlimFormerPlusPlus -f 0 \ |
| -chk checkpoint_best.pth -step_size 0.5 |
| ``` |
|
|
| The original dataset/model directory structure is preserved so the nnU-Net |
| predictor can discover the checkpoint. Downloading files does not install the |
| trainer or its CUDA dependencies. This model uses the project's custom nnU-Net |
| runtime; see the commands above for inference. |
|
|
| ## Intended use and limitations |
|
|
| The release is intended for noncommercial scientific research and reproducibility. |
| Its validation results describe the published dataset split and do not establish |
| performance on other scanners, institutions, populations or imaging protocols. |
| This release does not establish suitability for clinical diagnosis or deployment. |
| Dataset access and use remain subject to the dataset providers' terms. |
|
|
| ## License and provenance |
|
|
| **Noncommercial research use only.** The existing |
| [SlimFormer++ Noncommercial Research License 1.0](LICENSE) is preserved. |
| Third-party components retain their own licenses and notices; see |
| [NOTICE.md](NOTICE.md) and [Apache-2.0](licenses/Apache-2.0.txt). |
|
|
| [provenance.json](provenance.json) records the original release URL, archive |
| SHA256, checkpoint SHA256 and source revision. `SHA256SUMS` is the unmodified |
| checksum list from the original GitHub checkpoint archive. |
|
|
| ## Citation |
|
|
| Please cite SlimFormer++ together with nnU-Net: |
|
|
| ```bibtex |
| @misc{hong2026slimformertokenmixingmatters, |
| title={SlimFormer++: Token Mixing Where It Matters for Efficient 3D Medical Image Segmentation}, |
| author={Yang Hong and Lei Zhang and Zhihong Liu and Jianqing Mo and Xujiong Ye}, |
| year={2026}, |
| eprint={2610.25488}, |
| archivePrefix={arXiv}, |
| primaryClass={cs.CV}, |
| url={https://arxiv.org/abs/2610.25488}, |
| } |
| ``` |
|
|
| [nnU-Net source and citation](https://github.com/MIC-DKFZ/nnUNet) |
|
|
