Add ultrasound foundation-model TorchScript exports (USF-MAE, SAMUS, URFM, UltraFedFM)

LICENSE.md

@@ -0,0 +1,18 @@
+# Licenses
+
+There is no single license for `fideus-labs/impact-torchscript-models`: each model keeps the one
+its authors chose. The full texts are reproduced here, one per model.
+
+| Model | License | Text |
+| --- | --- | --- |
+| `US/USF-MAE` | MIT | [`LICENSES/USF-MAE-MIT.txt`](LICENSES/USF-MAE-MIT.txt) |
+| `US/SAMUS` | MIT | [`LICENSES/SAMUS-MIT.txt`](LICENSES/SAMUS-MIT.txt) |
+| `US/URFM` | MIT | [`LICENSES/URFM-MIT.txt`](LICENSES/URFM-MIT.txt) |
+| `US/UltraFedFM` | Apache-2.0 | [`LICENSES/UltraFedFM-Apache-2.0.txt`](LICENSES/UltraFedFM-Apache-2.0.txt) |
+
+These exports are derivative works: each wraps an encoder whose weights were
+trained and published by the authors named in
+[`NOTICE`](NOTICE), and the weights themselves are
+unmodified. The wrapper code around them (intensity normalization, layer
+selection, the IMPACT forward signature) is contributed to ImpactLoss under
+that project's license: <https://github.com/vboussot/ImpactLoss>

LICENSES/SAMUS-MIT.txt

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+MIT License
+
+Copyright (c) 2023 Xian Lin
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

LICENSES/URFM-MIT.txt

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+Provenance note (not part of the license text)
+==============================================
+URFM's weights live in the Hugging Face repository QingboKang/URFM, which is
+gated (`gated: auto`), so its LICENSE blob cannot be retrieved without a token
+and is not reproduced verbatim here. The license *claim* is public: the model
+card metadata carries `license: mit`, readable without authentication at
+https://huggingface.co/api/models/QingboKang/URFM (verified 2026-09-05).
+
+The standard MIT License text follows, with the copyright attributed to the
+URFM authors. Treat the upstream repository as authoritative:
+https://huggingface.co/QingboKang/URFM
+
+URFM is *not* redistributed in this Hugging Face repository — its export is
+built locally from the upstream checkpoint. This file records the license the
+export would carry.
+==============================================
+
+MIT License
+
+Copyright (c) 2025 Qingbo Kang, Qicheng Lao, Jun Gao, Wuyongga Bao, Zhu He,
+Chenlin Du, Qiang Lu, Kang Li (URFM authors)
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

LICENSES/USF-MAE-MIT.txt

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+MIT License
+
+Copyright (c) 2025 Youssef Megahed
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

LICENSES/UltraFedFM-Apache-2.0.txt

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NOTICE

@@ -0,0 +1,32 @@
+fideus-labs/impact-torchscript-models
+
+TorchScript feature extractors for ultrasound, packaged for ITKIMPACT.
+
+These exports are DERIVATIVE WORKS. Each wraps an encoder whose weights were
+trained and published by the authors named below; the weights themselves are
+unmodified. Each model keeps its upstream license, reproduced in
+LICENSES/ (see README.md for the per-model mapping).
+
+US/USF-MAE (MIT, redistributed here)
+  Original authors: Youssef Megahed, Robin Ducharme, Aylin Erman, Mark C. Walker, Steven Hawken, Adrian D. C. Chan
+  Source code:      https://github.com/Yusufii9/USF-MAE
+  License text:     LICENSES/USF-MAE-MIT.txt
+
+US/SAMUS (MIT, redistributed here)
+  Original authors: Xian Lin, Yangyang Xiang, Li Yu, Zengqiang Yan
+  Source code:      https://github.com/xianlin7/SAMUS
+  License text:     LICENSES/SAMUS-MIT.txt
+
+US/URFM (MIT, not redistributed here)
+  Original authors: Qingbo Kang, Qicheng Lao, Jun Gao, Wuyongga Bao, Zhu He, Chenlin Du, Qiang Lu, Kang Li
+  Source code:      https://huggingface.co/QingboKang/URFM
+  License text:     LICENSES/URFM-MIT.txt
+
+US/UltraFedFM (Apache-2.0, not redistributed here)
+  Original authors: Yuncheng Jiang, Chun-Mei Feng, Jinke Ren, Jun Wei, Zixun Zhang, Yiwen Hu, Yunbi Liu, Rui Sun, Xuemei Tang, Juan Du, Xiang Wan, Yong Xu, Bo Du, Xin Gao, Guangyu Wang, Shaohua Zhou, Shuguang Cui, Zhen Li
+  Source code:      https://github.com/yuncheng97/UltraFedFM
+  License text:     LICENSES/UltraFedFM-Apache-2.0.txt
+
+The wrapper code around these weights (intensity normalization, layer
+selection, the IMPACT forward signature) is contributed to ImpactLoss under
+that project's license: https://github.com/vboussot/ImpactLoss

README.md

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+---
+license: other
+license_name: per-model
+license_link: LICENSE.md
+tags:
+  - ultrasound
+  - itkimpact
+  - torchscript
+  - feature-extractor
+  - image-registration
+library_name: itk-impact
+---
+
+# IMPACT TorchScript feature extractors for ultrasound
+
+TorchScript **feature extractors** for 2D B-mode ultrasound, packaged for
+[ITKIMPACT](https://github.com/InsightSoftwareConsortium/ITKIMPACT) and
+[IMPACT](https://github.com/vboussot/ImpactLoss) — the registration metric that
+compares learned features instead of intensities. The layout mirrors
+[`VBoussot/impact-torchscript-models`](https://huggingface.co/VBoussot/impact-torchscript-models),
+the upstream repository of 30 general-purpose models: one `<Family>/<Name>.pt`
+per model plus a [`models.json`](models.json) describing it, so the
+same tooling reads both repositories.
+
+Each `.pt` wraps a published ultrasound foundation model's **encoder** in the
+IMPACT feature-extractor contract:
+
+```python
+forward(
+    x: torch.Tensor,          # (1, C, H, W)
+    nb_layers: torch.Tensor,  # how many feature maps to return
+    stats: torch.Tensor,      # (min, max, mean, std) from IMPACT, or empty
+    direction: torch.Tensor,  # unused by 2D models
+) -> list[torch.Tensor]       # exactly nb_layers maps, deepest last
+```
+
+IMPACT applies no intensity preprocessing, so each wrapper carries the model's
+own normalization: a raw B-mode frame goes in unchanged, single- or
+three-channel.
+
+> [!IMPORTANT]
+> These are **derivative works** — TorchScript exports of weights published by
+> the authors cited below, whose licenses are reproduced in
+> [`LICENSES/`](LICENSES/) and whose attribution is in
+> [`NOTICE`](NOTICE). The weights are unmodified; the
+> wrapper adds normalization, layer selection and the forward signature above.
+
+## Models
+
+| Model | Architecture | Feature layers | Dim | Input channels | `ImpactPatchSize` | License | In this repository |
+| --- | --- | --- | --- | --- | --- | --- | --- |
+| `US/USF-MAE` | MAE ViT-B/16 encoder, blocks 2/5/8/11 tapped as stride-16 feature maps | 4 maps, 768 channels each, stride 16 — taps after transformer blocks 2, 5, 8, 11 | 2D | 3 | `0 0` (whole frame), or any multiple of 16 | [MIT](LICENSES/USF-MAE-MIT.txt) | [`US/USF-MAE.pt`](US/USF-MAE.pt) |
+| `US/SAMUS` | SAM ViT-B image encoder at 256 px, patch size 8, global-attention blocks 2/5/8/11 plus the 256-channel neck | 5 maps on a 32x32 grid from the 256 px tile — 4 x 768-channel token maps after the global-attention blocks 2, 5, 8, 11 (the last after the `x + 0.5*cnnx` fusion) plus the 256-channel SAM neck | 2D | 1 | `256 256` **only** — the traced graph is valid at that size | [MIT](LICENSES/SAMUS-MIT.txt) | [`US/SAMUS.pt`](US/SAMUS.pt) |
+| `US/URFM` | MAE ViT-B/16 encoder with a BiomedCLIP representation target, blocks 2/5/8/11 tapped as stride-16 feature maps | 4 maps, 768 channels each, stride 16 — taps after transformer blocks 2, 5, 8, 11 | 2D | 3 | `0 0` (whole frame), or any multiple of 16 | [MIT](LICENSES/URFM-MIT.txt) | build it yourself |
+| `US/UltraFedFM` | Federated MAE ViT-B/16 encoder, blocks 2/5/8/11 tapped as stride-16 feature maps | 4 maps, 768 channels each, stride 16 — taps after transformer blocks 2, 5, 8, 11 | 2D | 3 | `0 0` (whole frame), or any multiple of 16 | [Apache-2.0](LICENSES/UltraFedFM-Apache-2.0.txt) | build it yourself |
+
+### Provenance
+
+| Model | Source code | Original weights | Paper |
+| --- | --- | --- | --- |
+| `US/USF-MAE` | <https://github.com/Yusufii9/USF-MAE> | <https://drive.google.com/file/d/1ZPu_7KhMEuaq-XdLhVp2EEgMgLJ4dKhr/view> | <https://doi.org/10.1016/j.bspc.2026.110313> |
+| `US/SAMUS` | <https://github.com/xianlin7/SAMUS> | <https://drive.google.com/file/d/1nQjMAvbPeolNpCxQyU_HTiOiB5704pkH/view> | <https://doi.org/10.48550/arXiv.2309.06824> |
+| `US/URFM` | <https://huggingface.co/QingboKang/URFM> | <https://huggingface.co/QingboKang/URFM/blob/main/mae_vit_base_patch16_dec768d8b_all_biomedclip_1199.pth> | <https://doi.org/10.1016/j.isci.2025.112917> |
+| `US/UltraFedFM` | <https://github.com/yuncheng97/UltraFedFM> | <https://drive.google.com/file/d/13cczqVFk84c_9QDP2OLURU1jWryCky5f/view> | <https://doi.org/10.1038/s41746-025-02085-0> |
+
+### Not redistributed here
+
+Catalogued, licensed and buildable, but not shipped here: `US/URFM`, `US/UltraFedFM`.
+Their upstream checkpoints sit behind a sign-in — a gated Hugging Face
+repository, and cloud shares scoped to specific accounts — so nothing
+here can fetch them for you. Build them yourself with the scripts in
+`Data/Models/builds/US/` (see **Reproducing these exports** below); the
+exports drop straight into the same `US/` layout.
+
+## Usage — Python
+
+```python
+import itk
+import torch
+from huggingface_hub import hf_hub_download
+
+model_path = hf_hub_download(
+    repo_id="fideus-labs/impact-torchscript-models",
+    filename="US/USF-MAE.pt",
+    local_dir="models",
+)
+
+frame = itk.imread("bmode.mha", itk.F)  # a 2D itk.Image[itk.F, 2] B-mode frame
+
+# Voxel size = the frame's own spacing, so IMPACT does not resample and the
+# feature image lands pixel-for-pixel on the input grid.
+config = itk.ModelConfiguration(
+    model_path,                                 # TorchScript file
+    2,                                          # dimension        (models.json)
+    3,                                          # numberofchannels (models.json)
+    [0, 0],                                     # patch size; 0 0 = whole frame
+    [float(spacing) for spacing in frame.GetSpacing()],
+    0,                                          # patch overlap
+    [False, False, False, True],                # layers mask: ask for 4, keep the deepest
+    False,                                      # useMixedPrecision
+)
+
+interpolator = itk.BSplineInterpolateImageFunction[type(frame), itk.D, itk.F]
+extractor = itk.ImageToFeaturesMap[type(frame), interpolator].New()
+extractor.SetModelConfiguration(config)
+extractor.SetDevice("cuda:0" if torch.cuda.is_available() else "cpu")
+extractor.AddInput(frame)
+extractor.Update()
+
+features = extractor.GetOutput(0)  # itk.VectorImage, 768 components per pixel
+```
+
+The **layers mask does double duty**: its length is how many feature maps
+IMPACT asks the model for, and each `1` keeps that map. `[0, 0, 0, 1]` on a
+4-layer model therefore means "compute all four, keep the deepest".
+
+For `US/SAMUS`, pass `1` input channel, `[256, 256]` as the patch size, and a
+**5**-element mask (`[0, 0, 0, 0, 1]` keeps the 256-channel SAM neck).
+
+As a registration loss in PyTorch, with
+[ImpactLoss](https://github.com/vboussot/ImpactLoss):
+
+```python
+from IMPACT import IMPACTReg
+
+loss = IMPACTReg(
+    "US/USF-MAE.pt",
+    shape=[0, 0],
+    in_channels=1,
+    weights=[0, 0, 0, 1],
+    repo_id="fideus-labs/impact-torchscript-models",
+)
+```
+
+## Usage — Elastix
+
+One resolution of `ParameterMaps/ParameterMap_US_2D_Static.txt`
+(`ImpactVoxelSize` is the spacing the frame is resampled to; one token spans
+16 voxels, so this level extracts features at 4 mm per token — roughly the
+native spacing of a curvilinear B-mode frame):
+
+```txt
+(Metric "Impact")
+(ImpactMode "Static")
+
+(ImpactModelsPath0 "/Data/Models/US/USF-MAE.pt")
+(ImpactDimension0 2)
+(ImpactNumberOfChannels0 3)
+(ImpactPatchSize0 0 0)
+(ImpactVoxelSize0 0.25 0.25)
+(ImpactLayersMask0 "0001")
+(ImpactPCA0 0)
+(ImpactSubsetFeatures0 64)
+(ImpactDistance0 "L2")
+(ImpactLayersWeight0 1)
+```
+
+`ImpactModelsPath` is resolved inside the IMPACT model directory, so
+`US/USF-MAE.pt` sits where `sh Data/Models/download_models.sh` puts it. The
+mask is a string here and one bit long per feature layer: 4 bits for the MAE
+models, 5 for `US/SAMUS`, which also needs `(ImpactPatchSize 256 256)` and
+`(ImpactNumberOfChannels 1)`.
+
+## Reproducing these exports
+
+Every model here is built from its upstream checkpoint by a standalone script
+in `Data/Models/builds/US/` of
+[ImpactLoss](https://github.com/vboussot/ImpactLoss) — download, wrap, check
+the layer contract, `torch.jit` export. The scripts import only `torch`,
+`gdown` and `huggingface_hub`, and write `./<Name>.pt` into the working
+directory, so a rebuild is:
+
+```bash
+cd Data/Models/builds/US
+python USF-MAE.py    # -> ./USF-MAE.pt
+```
+
+The exports are reproducible in *behaviour*, not byte-for-byte: TorchScript
+archives differ between runs, but a rebuilt model returns feature maps
+identical to these (max absolute difference 0.0, checked across every layer
+count, several input shapes and both normalization paths).
+
+## Licensing
+
+There is no single license: each model keeps the one its authors chose.
+The texts are in [`LICENSES/`](LICENSES/), one per model,
+indexed by [`LICENSE.md`](LICENSE.md), and
+[`NOTICE`](NOTICE) names the original authors. The
+`license: other` / `license_name: per-model` front matter above says the same
+thing in Hugging Face's vocabulary.
+
+Models whose licenses did **not** permit redistribution — several published
+ultrasound foundation models are CC BY-NC or carry no license at all — were
+excluded rather than repackaged.
+
+## Integrity
+
+[`SHA256SUMS`](SHA256SUMS) covers every other file:
+
+```bash
+sha256sum -c SHA256SUMS
+```
+
+## Citation
+
+Cite the original work, not this packaging:
+
+```bibtex
+@article{megahed2026usfmae,
+  title   = {USF-MAE: Ultrasound self-supervised foundation model with masked autoencoding},
+  author  = {Megahed, Youssef and Ducharme, Robin and Erman, Aylin and
+             Walker, Mark C. and Hawken, Steven and Chan, Adrian D. C.},
+  journal = {Biomedical Signal Processing and Control},
+  volume  = {122},
+  pages   = {110313},
+  year    = {2026},
+  doi     = {10.1016/j.bspc.2026.110313}
+}
+```
+
+```bibtex
+@misc{lin2023samus,
+  title        = {SAMUS: Adapting Segment Anything Model for Clinically-Friendly and
+                  Generalizable Ultrasound Image Segmentation},
+  author       = {Lin, Xian and Xiang, Yangyang and Yu, Li and Yan, Zengqiang},
+  year         = {2023},
+  eprint       = {2309.06824},
+  archivePrefix = {arXiv},
+  primaryClass = {cs.CV},
+  doi          = {10.48550/arXiv.2309.06824},
+  note         = {Later arXiv versions are titled "Beyond Adapting SAM: Towards
+                  End-to-End Ultrasound Image Segmentation via Auto Prompting"}
+}
+```
+
+```bibtex
+@article{kang2025urfm,
+  title   = {URFM: A general Ultrasound Representation Foundation Model for advancing
+             ultrasound image diagnosis},
+  author  = {Kang, Qingbo and Lao, Qicheng and Gao, Jun and Bao, Wuyongga and He, Zhu and
+             Du, Chenlin and Lu, Qiang and Li, Kang},
+  journal = {iScience},
+  volume  = {28},
+  number  = {8},
+  pages   = {112917},
+  year    = {2025},
+  doi     = {10.1016/j.isci.2025.112917}
+}
+```
+
+```bibtex
+@article{jiang2025ultrafedfm,
+  title   = {From pretraining to privacy: federated ultrasound foundation model with
+             self-supervised learning},
+  author  = {Jiang, Yuncheng and Feng, Chun-Mei and Ren, Jinke and Wei, Jun and
+             Zhang, Zixun and Hu, Yiwen and Liu, Yunbi and Sun, Rui and Tang, Xuemei and
+             Du, Juan and Wan, Xiang and Xu, Yong and Du, Bo and Gao, Xin and
+             Wang, Guangyu and Zhou, Shaohua and Cui, Shuguang and Li, Zhen},
+  journal = {npj Digital Medicine},
+  volume  = {8},
+  number  = {1},
+  year    = {2025},
+  doi     = {10.1038/s41746-025-02085-0}
+}
+```

SHA256SUMS

@@ -0,0 +1,10 @@
+11335f24def485b29b270936ea7a1e34880f7360c0db24a63127e752347ec458  US/USF-MAE.pt
+5d0010dad0ba9512a03c2a127ae1acb9b11f57ce06b381c88c2c4bce7d29b2f1  US/SAMUS.pt
+8fe71fe20dec47a6473a402dd81e6c590276191f54c059684c64847022850455  models.json
+a9db86fbbef06c3657bb95867eba091e3a179f108c0e0a6d9509bfbe4918a8d2  LICENSES/USF-MAE-MIT.txt
+88430d394e3a094b141f5968b9afdee3abb47460f6b67594838b80056b72439e  LICENSES/SAMUS-MIT.txt
+1882cb94c8d99452dce5548334f25810eb85b24df0ef51760ab0851eacdcf958  LICENSES/URFM-MIT.txt
+c71d239df91726fc519c6eb72d318ec65820627232b2f796219e87dcf35d0ab4  LICENSES/UltraFedFM-Apache-2.0.txt
+53008676704421836a8e8733f1fd053613f7e7e8f5fabf5f64db31b5d71397ee  LICENSE.md
+bd7cec35d20a4be5bfe264e7a1f58f3e663f3bf1ff89fdfce7eac2b41536b45f  NOTICE
+01c1c6900e1705c9485f044593a44074bd63cb9210a6ebad7c462a1df360bb10  README.md

US/SAMUS.pt

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+oid sha256:5d0010dad0ba9512a03c2a127ae1acb9b11f57ce06b381c88c2c4bce7d29b2f1
+size 517455255

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+size 343275843

models.json

@@ -0,0 +1,40 @@
+{
+  "US/USF-MAE.pt": {
+    "fov": {
+      "formula": "Global(ViT-B/16, stride 16)"
+    },
+    "dimension": "2",
+    "numberofchannels": "3",
+    "layers": 4,
+    "license": "MIT",
+    "patch_size": null,
+    "size_multiple": 16,
+    "source": {
+      "name": "USF-MAE",
+      "code": "https://github.com/Yusufii9/USF-MAE",
+      "weights": "https://drive.google.com/file/d/1ZPu_7KhMEuaq-XdLhVp2EEgMgLJ4dKhr/view",
+      "paper": "https://doi.org/10.1016/j.bspc.2026.110313",
+      "architecture": "MAE ViT-B/16 encoder, blocks 2/5/8/11 tapped as stride-16 feature maps",
+      "checkpoint_sha256": "f815c629878c17136985af9f4fdc81c2cfa02a94e4d992c026699957f75ccb66"
+    }
+  },
+  "US/SAMUS.pt": {
+    "fov": {
+      "formula": "Global(256 px tile, stride 8)"
+    },
+    "dimension": "2",
+    "numberofchannels": "1",
+    "layers": 5,
+    "license": "MIT",
+    "patch_size": 256,
+    "size_multiple": null,
+    "source": {
+      "name": "SAMUS",
+      "code": "https://github.com/xianlin7/SAMUS",
+      "weights": "https://drive.google.com/file/d/1nQjMAvbPeolNpCxQyU_HTiOiB5704pkH/view",
+      "paper": "https://doi.org/10.48550/arXiv.2309.06824",
+      "architecture": "SAM ViT-B image encoder at 256 px, patch size 8, global-attention blocks 2/5/8/11 plus the 256-channel neck",
+      "checkpoint_sha256": "a95de25d7b9065a92e268d73b1e76b871d7b0e4e66ce872b1e298f5b6c446fc4"
+    }
+  }
+}