Initial upload via tools/push_to_hf.py (architecture: ilex.models.triad.TriadPlainConvUNet)

README.md

@@ -14,7 +14,7 @@ license_link: https://github.com/wangshansong1/Triad/blob/main/LICENSE
 
 ## Description
 
-Triad vision foundation model for 3D MRI, ported to JAX / Equinox from the upstream PyTorch release. Triad is an nnUNet PlainConvEncoder pretrained self-supervised on Triad-131K (131,170 3D MRI volumes spanning brain, breast, and prostate; T1/T2/FLAIR/DWI/fMRI/DCE) and serves as a transfer-learning backbone for downstream MRI segmentation, classification, and registration. The published checkpoints are encoder-only (the self-supervised decoder / mask token are stripped); this port exposes the pretrained encoder, whose multi-scale skip features are the transfer representation. Two pretraining objectives are released as separate bundles: masked autoencoding (MAE) and SimMIM. The alternative Swin-B backbone variants are out of scope for v0 (pending a 3D Swin-UNETR primitive).
+Triad vision foundation model for 3D MRI, ported to JAX / Equinox from the upstream PyTorch release. Triad is an nnUNet PlainConvEncoder pretrained self-supervised on Triad-131K (131,170 3D MRI volumes spanning brain, breast, and prostate; T1/T2/FLAIR/DWI/fMRI/DCE) and serves as a transfer-learning backbone for downstream MRI segmentation, classification, and registration. The published checkpoints are encoder-only (the self-supervised decoder / mask token are stripped); this port exposes the pretrained encoder, whose multi-scale features are the transfer representation. Two backbone families are ported: the nnUNet PlainConvUNet encoder (TriadPlainConvUNet) and the 3D Swin Transformer encoder (TriadSwinViT, the Swin-B variant, via the shared nimox SwinViT primitive). Each is released under two self-supervised objectives -- masked autoencoding (MAE) and SimMIM -- as separate bundles (four in total).
 
 ## Intended use
 

config.json

@@ -19,7 +19,7 @@
   "authors": "Wang S., et al.",
   "copyright": "Network architecture and pretrained weights: copyright (c) the Triad authors, released under the MIT License. JAX / Equinox port: copyright (c) the ilex authors, released under the Apache-2.0 / GPL-3.0 dual license used by ilex itself.",
   "data_type": "nibabel",
-  "description": "Triad vision foundation model for 3D MRI, ported to JAX / Equinox from the upstream PyTorch release. Triad is an nnUNet PlainConvEncoder pretrained self-supervised on Triad-131K (131,170 3D MRI volumes spanning brain, breast, and prostate; T1/T2/FLAIR/DWI/fMRI/DCE) and serves as a transfer-learning backbone for downstream MRI segmentation, classification, and registration. The published checkpoints are encoder-only (the self-supervised decoder / mask token are stripped); this port exposes the pretrained encoder, whose multi-scale skip features are the transfer representation. Two pretraining objectives are released as separate bundles: masked autoencoding (MAE) and SimMIM. The alternative Swin-B backbone variants are out of scope for v0 (pending a 3D Swin-UNETR primitive).",
+  "description": "Triad vision foundation model for 3D MRI, ported to JAX / Equinox from the upstream PyTorch release. Triad is an nnUNet PlainConvEncoder pretrained self-supervised on Triad-131K (131,170 3D MRI volumes spanning brain, breast, and prostate; T1/T2/FLAIR/DWI/fMRI/DCE) and serves as a transfer-learning backbone for downstream MRI segmentation, classification, and registration. The published checkpoints are encoder-only (the self-supervised decoder / mask token are stripped); this port exposes the pretrained encoder, whose multi-scale features are the transfer representation. Two backbone families are ported: the nnUNet PlainConvUNet encoder (TriadPlainConvUNet) and the 3D Swin Transformer encoder (TriadSwinViT, the Swin-B variant, via the shared nimox SwinViT primitive). Each is released under two self-supervised objectives -- masked autoencoding (MAE) and SimMIM -- as separate bundles (four in total).",
   "equinox_version": "0.13.8",
   "ilex_version": "0.0.0.dev0",
   "image_classes": "Single-channel 3D MRI volume (contrast-general; pretrained across T1, T2, FLAIR, DWI, fMRI, DCE).",

model.safetensors

@@ -1,3 +1,3 @@
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