Name every model's source and terms, and carry those licenses in full

NOTICE now covers the whole repository -- TotalSegmentator, MRSegmentator, SAM 2, DINOv2, VGG16 and MIND alongside anatomix -- with the upstream project, its license and its paper for each. LICENSES carries the Apache-2.0 and BSD-3-Clause texts those terms require to be distributed with the work.

Two limits are written down where they can be seen: TotalSegmentator offers part of its tasks under Apache-2.0 and the rest behind a license number, and only the open ones (total, total_mr, lung_vessels) are exported here; DINOv3's checkpoints fall under the DINOv3 License Agreement, so its export script builds them locally instead.

LICENSES/Apache-2.0.txt

@@ -0,0 +1,203 @@
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LICENSES/torchvision-BSD-3-Clause.txt

@@ -0,0 +1,29 @@
+BSD 3-Clause License
+
+Copyright (c) Soumith Chintala 2016, 
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+* Redistributions of source code must retain the above copyright notice, this
+  list of conditions and the following disclaimer.
+
+* Redistributions in binary form must reproduce the above copyright notice,
+  this list of conditions and the following disclaimer in the documentation
+  and/or other materials provided with the distribution.
+
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+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

NOTICE

@@ -1,27 +1,75 @@
 Third-party material redistributed in this repository
 =====================================================
 
+Every model here is a TorchScript export of a pretrained network published by someone else. The
+weights are unchanged; what this repository adds is a wrapper that normalises the input from the
+statistics IMPACT passes and returns one tensor per feature layer. The export scripts are published in
+https://github.com/vboussot/ImpactLoss under Data/Models/builds/.
+
 Anatomix/Anatomix.pt, Anatomix/AnatomixDevViT.pt
 ------------------------------------------------
-TorchScript exports of the anatomix pretrained networks.
-
   Weights:  https://huggingface.co/neeldey/anatomix (anatomix.pth, anatomix-dev-vit.pth)
   Code:     https://github.com/neel-dey/anatomix
-  License:  MIT, Copyright 2024 Neel Dey -- see LICENSES/anatomix-MIT.txt
-  Paper:    Dey et al., Learning General-purpose Biomedical Volume Representations
-            using Randomized Synthesis, ICLR 2025, arXiv:2411.02372
+  License:  MIT, Copyright 2024 Neel Dey -- LICENSES/anatomix-MIT.txt
+  Paper:    Dey et al., Learning General-purpose Biomedical Volume Representations using Randomized
+            Synthesis, ICLR 2025, arXiv:2411.02372
 
-AnatomixDevViT additionally builds on PrimusV2 from dynamic-network-architectures,
-which anatomix configures and extends (output normalisation, register-token
-initialisation, QK normalisation):
+AnatomixDevViT additionally builds on PrimusV2 from dynamic-network-architectures, which anatomix
+configures and extends (output normalisation, register-token initialisation, QK normalisation):
 
   Code:     https://github.com/MIC-DKFZ/dynamic-network-architectures
-  License:  Apache License 2.0, Copyright 2022 Division of Medical Image Computing,
-            German Cancer Research Center (DKFZ) -- see
-            LICENSES/dynamic-network-architectures-Apache-2.0.txt
-
-Changes made here: each network is traced in evaluation mode and wrapped in a
-scripted module that normalises the input from the statistics IMPACT passes and
-returns one tensor per feature layer. The weights themselves are unchanged. The
-export scripts are published in https://github.com/vboussot/ImpactLoss under
-Data/Models/builds/Anatomix/.
+  License:  Apache License 2.0, Copyright 2022 Division of Medical Image Computing, German Cancer
+            Research Center (DKFZ) -- LICENSES/dynamic-network-architectures-Apache-2.0.txt
+
+TS/M258.pt, TS/M291-M295.pt, TS/M297-M298.pt, TS/M730-M733.pt, TS/M850-M853.pt
+-------------------------------------------------------------------------------
+  Weights:  TotalSegmentator, https://github.com/wasserth/TotalSegmentator
+  License:  Apache License 2.0 -- LICENSES/Apache-2.0.txt
+  Paper:    Wasserthal et al., TotalSegmentator: robust segmentation of 104 anatomic structures in CT
+            images, Radiology: Artificial Intelligence, 2023, arXiv:2208.05868
+
+  TotalSegmentator publishes its tasks under two different terms. The models here come from the tasks
+  it offers under Apache 2.0: `total` (M291-M295, M297, M298), `total_mr` (M730-M733 in the 2.2/2.4
+  series, M850-M853 since) and `lung_vessels` (M258). Its other tasks -- heartchambers_highres,
+  tissue_types, brain_structures, appendicular_bones, face, thigh_shoulder_muscles, coronary_arteries
+  and the rest of that list -- need a license number from the authors, free for non-commercial use and
+  paid otherwise, and their weights must NOT be redistributed here.
+
+MRSeg/MRSeg.pt
+--------------
+  Weights:  MRSegmentator, https://github.com/hhaentze/MRSegmentator
+  License:  Apache License 2.0 -- LICENSES/Apache-2.0.txt
+  Paper:    Haentze et al., MRSegmentator: Multi-Modality Segmentation of 40 Classes in MRI and CT,
+            2024, arXiv:2405.06463
+
+SAM2.1/SAM2.1_Small.pt, SAM2.1/SAM2.1_Tiny.pt
+----------------------------------------------
+  Weights:  SAM 2, https://github.com/facebookresearch/sam2
+  License:  Apache License 2.0, Copyright (c) Meta Platforms, Inc. and affiliates --
+            LICENSES/Apache-2.0.txt
+  Paper:    Ravi et al., SAM 2: Segment Anything in Images and Videos, 2024, arXiv:2408.00714
+
+Dino/DinoV2_Small.pt
+--------------------
+  Weights:  DINOv2, https://github.com/facebookresearch/dinov2
+  License:  Apache License 2.0, Copyright (c) Meta Platforms, Inc. and affiliates --
+            LICENSES/Apache-2.0.txt
+  Paper:    Oquab et al., DINOv2: Learning Robust Visual Features without Supervision, 2023,
+            arXiv:2304.07193
+
+  DINOv3 is NOT redistributed here: its checkpoints fall under the DINOv3 License Agreement rather than
+  an open-source license. The export script in ImpactLoss builds them locally from your own download.
+
+VGG/VGG16.pt
+------------
+  Weights:  torchvision, https://github.com/pytorch/vision
+  License:  BSD 3-Clause, Copyright (c) Soumith Chintala 2016 --
+            LICENSES/torchvision-BSD-3-Clause.txt
+  Paper:    Simonyan and Zisserman, Very Deep Convolutional Networks for Large-Scale Image
+            Recognition, 2014, arXiv:1409.1556
+
+MIND/*.pt
+---------
+  No third-party weights: MIND is a handcrafted descriptor, implemented here from its paper.
+  Paper:    Heinrich et al., MIND: Modality independent neighbourhood descriptor for multi-modal
+            deformable registration, Medical Image Analysis, 2012, doi:10.1016/j.media.2012.05.008

README.md

@@ -54,7 +54,13 @@ argument, its normalisation branch was frozen on the image's own min and max, so
 passes were ignored. Scored with statistics, the two exports differ by about 7e-3; without them they
 agree to 9e-6.
 
-Redistribution terms and the export scripts are listed in `NOTICE`; the scripts themselves live in
+`NOTICE` names, for every model here, where its weights come from and under which terms, and
+`LICENSES/` carries those licenses in full. Two of them bound what may be added to this repository:
+TotalSegmentator publishes part of its tasks under Apache 2.0 and the rest behind a license number, of
+which only the open ones are exported here; and DINOv3's checkpoints are covered by the DINOv3 License
+Agreement, so they are built locally rather than redistributed.
+
+The export scripts live in
 [ImpactLoss/Data/Models/builds/Anatomix](https://github.com/vboussot/ImpactLoss/tree/main/Data/Models/builds/Anatomix).
 
 ---
@@ -80,7 +86,7 @@ In addition, the repository also includes:
 |----------------|---------------------------------------|-------------------------------------------------------------|------------------------|--------------|---------------|
 | **MIND**       | Handcrafted descriptor                | [Heinrich et al., 2012](https://doi.org/10.1016/j.media.2012.05.008) | `2*r*d + 1` (r: radius, d: dilation)               | Apache 2.0 | Normalize intensities to [0, 1] |
 | **SAM2.1**     | General segmentation (natural images) | [Ravi et al., 2023](https://arxiv.org/abs/2408.00714)       | 29                    | Apache 2.0           | Normalize intensities to [0, 1], then standardize with mean 0.485 and std 0.229 |
-| **TS Models**  | CT/MRI segmentation                   | [Wasserthal et al., 2022](https://arxiv.org/abs/2208.05868)  | `2^l + 3` (l: layer number)              | Apache 2.0    | Canonical orientation for all models. For MRI models (e.g., TS/M730–M733-M850–M853), standardize intensities to zero mean and unit variance. For CT models (e.g., TS/M258, TS/M291), clip intensities + normalize model dependant |
+| **TS Models**  | CT/MRI segmentation                   | [Wasserthal et al., 2022](https://arxiv.org/abs/2208.05868)  | `2^l + 3` (l: layer number)              | Apache 2.0 (open tasks only, see `NOTICE`) | Canonical orientation for all models. For MRI models (e.g., TS/M730–M733-M850–M853), standardize intensities to zero mean and unit variance. For CT models (e.g., TS/M258, TS/M291), clip intensities + normalize model dependant |
 | **MRSegmentator**  | CT/MRI segmentation                   | [Häntze et al., 2024](https://arxiv.org/abs/2405.06463)  | `2^l + 3` (l: layer number)              | Apache 2.0    | Standardize intensities to zero mean and unit variance.|
 | **Anatomix**   | Anatomy-aware encoder (U-Net, 16 features) | [Dey et al., 2024](https://arxiv.org/abs/2411.02372)   | Global(Static mode)          | MIT           | Normalize intensities to [0, 1] |
 | **AnatomixDevViT** | Anatomy-aware encoder (ViT, 32 features) | [Dey et al., 2024](https://arxiv.org/abs/2411.02372)  | 128 (fixed input)     | MIT + Apache 2.0 | Normalize intensities to [0, 1] |