Update CKPT to allow weights_only=True loading and add config.json for download tracking

adaptation_plan.json

@@ -54,9 +54,9 @@
                     1
                 ],
                 "patch_size": [
-                    64,
-                    64,
-                    64
+                    160,
+                    160,
+                    160
                 ]
             }
         },
@@ -79,29 +79,29 @@
         {
             "type": "Architecture",
             "name": "ResEncL",
-            "bibtex_citations": [
-                "@inproceedings{isensee2024nnu,\n                title={nnu-net revisited: A call for rigorous validation in 3d medical image segmentation},\n                author={Isensee, Fabian and Wald, Tassilo and Ulrich, Constantin and Baumgartner, Michael and Roy, Saikat and Maier-Hein, Klaus and Jaeger, Paul F},\n                booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},\n                pages={488--498},\n                year={2024},\n                organization={Springer}\n                }"
+            "apa_citations": [
+                "Isensee, F., Wald, T., Ulrich, C., Baumgartner, M., Roy, S., Maier-Hein, K., & Jaeger, P. F. (2024, October). nnu-net revisited: A call for rigorous validation in 3d medical image segmentation. MICCAI."
             ]
         },
         {
             "type": "Pretraining Method",
             "name": "Volume Fusion",
-            "bibtex_citations": [
-                "@article{wang2023mis,\n  title={Mis-fm: 3d medical image segmentation using foundation models pretrained on a large-scale unannotated dataset},\n  author={Wang, Guotai and Wu, Jianghao and Luo, Xiangde and Liu, Xinglong and Li, Kang and Zhang, Shaoting},\n  journal={arXiv preprint arXiv:2306.16925},\n  year={2023}\n}"
+            "apa_citations": [
+                "Wang, G., Wu, J., Luo, X., Liu, X., Li, K., & Zhang, S. (2023). Mis-fm: 3d medical image segmentation using foundation models pretrained on a large-scale unannotated dataset. arXiv preprint arXiv:2306.16925."
             ]
         },
         {
             "type": "Pre-Training Dataset",
             "name": "OpenMind",
-            "bibtex_citations": [
-                "@article{wald2024openmind,\n    title={An OpenMind for 3D medical vision self-supervised learning},\n    author={Wald, Tassilo and Ulrich, Constantin and Suprijadi, Jonathan and Ziegler, Sebastian and Nohel, Michal and Peretzke, Robin and K{\"o}hler, Gregor and Maier-Hein, Klaus H},\n    journal={arXiv preprint arXiv:2412.17041},\n    year={2024}\n    }\n    "
+            "apa_citations": [
+                "Wald, T., Ulrich, C., Suprijadi, J., Ziegler, S., Nohel, M., Peretzke, R., ... & Maier-Hein, K. H. (2024). An OpenMind for 3D medical vision self-supervised learning. arXiv preprint arXiv:2412.17041."
             ]
         },
         {
             "type": "Framework",
             "name": "nnssl",
-            "bibtex_citations": [
-                "@article{wald2024revisiting,\n  title={Revisiting MAE pre-training for 3D medical image segmentation},\n  author={Wald, Tassilo and Ulrich, Constantin and Lukyanenko, Stanislav and Goncharov, Andrei and Paderno, Alberto and Maerkisch, Leander and J{\"a}ger, Paul F and Maier-Hein, Klaus},\n  journal={arXiv preprint arXiv:2410.23132},\n  year={2024}\n}"
+            "apa_citations": [
+                "Wald, T., Ulrich, C., Lukyanenko, S., Goncharov, A., Paderno, A., Maerkisch, L., ... & Maier-Hein, K. (2024). Revisiting MAE pre-training for 3D medical image segmentation. CVPR."
             ]
         }
     ],

checkpoint_final.pth

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:4ff1855da724cb9ed00eafccc571e6211f998b01c634de17510eea7fb4332b3a
-size 491211854
+oid sha256:b067720b1846376e0fa5ebd45a4bb42977ce5fcfec003f848e495715b740d369
+size 491210894

config.json

@@ -0,0 +1,3 @@
+{
+    "description": "Dummy config to allow tracking HF downloads."
+}