Update README.md

README.md

@@ -1,44 +1,216 @@
 ---
-license: mit
+license: cc-by-4.0
 library_name: pytorch
+datasets:
+  - MedPhyGraph/support-graph-data
 tags:
   - digital-twins
   - scene-graphs
+  - dynamic-scene-graphs
   - robotics
-  - computer-vision
-  - graph-neural-network
+  - support-relations
+  - graph-maintenance
+  - counterfactual-evidence
+  - pytorch
 ---
 
 # CF-SupportNet
 
-CF-SupportNet is the learned edge-scoring component of **MedPhyGraph**
-(TwinWorld: Visual Intelligence for Built Environment Digital Twins, ECCV 2026 Workshop). It is NOT the full MedPhyGraph
-framework by itself: MedPhyGraph also applies deterministic State
-Consistency and Union-Based Transition-Aware Consistency (non-learned,
-code-only) on top of these scores to produce the final maintained
-support graph. See the paper and code at
-https://medphygraph.github.io/ for the complete pipeline.
+CF-SupportNet is the learned edge-scoring component of **MedPhyGraph**  
+(*TwinWorld: Visual Intelligence for Built Environment Digital Twins, ECCV 2026 Workshop*).
 
-## Files
+It is **not** the full MedPhyGraph framework by itself. MedPhyGraph also applies
+deterministic **State Consistency** and **Union-Based Transition-Aware
+Consistency** on top of CF-SupportNet scores to maintain support relations
+across adjacent digital-twin states.
 
-- `health_dyphygraph_r1.0_seed0.pt` — primary checkpoint used for all
-  headline paper results (rho=1.0, seed 0)
-- `health_dyphygraph_r1.0_seed{1-4}.pt` — used only for the paper's
-  multi-seed reproducibility audit; not intended as alternative
-  production checkpoints
+| Resource | Link |
+|---|---|
+| Model | **You are here** |
+| Dataset | [MedPhyGraph/support-graph-data](https://huggingface.co/datasets/MedPhyGraph/support-graph-data) |
+| Project | [medphygraph.github.io](https://medphygraph.github.io/) |
+
+## Overview
+
+CF-SupportNet scores candidate `SupportedBy` edges from structured scene
+information.
+
+The scorer combines:
+
+- static geometric features
+- candidate support-edge features
+- analytic host-removal counterfactual evidence
+
+The counterfactual signal is computed using an **analytic AABB-based geometric
+proxy**. It should not be interpreted as a full rigid-body physics simulation.
+
+CF-SupportNet operates downstream of scene perception. Rendered RGB images are
+**not** model inputs and are **not** used as label sources.
 
 ## Architecture
 
-One-layer GRU (hidden width 64), fusion MLP, 25,409 trainable
-parameters. Full training details in the paper, Section 3.4.
+The released CF-SupportNet checkpoints use the paper-frozen configuration:
+
+| Property | Value |
+|---|---|
+| Framework | PyTorch |
+| Architecture | GRU + MLP |
+| Hidden size | 64 |
+| Trainable parameters | 25,409 |
+| Counterfactual weight `rho` | 1.0 |
+
+## Paper-frozen checkpoints
+
+This repository contains five released checkpoints:
+
+| Checkpoint | Role |
+|---|---|
+| `health_dyphygraph_r1.0_seed0.pt` | Primary paper checkpoint |
+| `health_dyphygraph_r1.0_seed1.pt` | Multi-seed checkpoint |
+| `health_dyphygraph_r1.0_seed2.pt` | Multi-seed checkpoint |
+| `health_dyphygraph_r1.0_seed3.pt` | Multi-seed checkpoint |
+| `health_dyphygraph_r1.0_seed4.pt` | Multi-seed checkpoint |
+
+**Seed 0** is the primary paper checkpoint.  
+**Seeds 1–4** are provided for the paper's multi-seed evaluation.
+
+The checkpoint files are paper-frozen artifacts and should not be modified when
+reproducing the released results.
+
+## Download
+
+Download the complete model release with the Hugging Face CLI:
+
+```bash
+hf download MedPhyGraph/CF-SupportNet \
+  --local-dir ./CF-SupportNet
+```
+
+Or with Python:
+
+```python
+from huggingface_hub import snapshot_download
+
+snapshot_download(
+    repo_id="MedPhyGraph/CF-SupportNet",
+    local_dir="./CF-SupportNet",
+)
+```
+
+To download only the primary checkpoint:
+
+```python
+from huggingface_hub import hf_hub_download
+
+checkpoint_path = hf_hub_download(
+    repo_id="MedPhyGraph/CF-SupportNet",
+    filename="health_dyphygraph_r1.0_seed0.pt",
+)
+
+print(checkpoint_path)
+```
+
+## Relationship to MedPhyGraph
+
+CF-SupportNet provides learned scores for candidate support edges.
+
+MedPhyGraph then applies deterministic graph-maintenance components, including:
+
+1. **State Consistency**
+2. **Union-Based Transition-Aware Consistency**
+
+These components are non-learned inference operations and are not encoded as
+separate model checkpoints.
+
+In other words:
+
+```text
+structured scene state
+        │
+        ▼
+candidate support edges
+        │
+        ▼
+CF-SupportNet
+(learned edge scores)
+        │
+        ▼
+State Consistency
+        │
+        ▼
+Union-Based Transition-Aware Consistency
+        │
+        ▼
+maintained SupportedBy graph
+```
+
+## Dataset
+
+The corresponding public procedural training and evaluation data are available
+at:
+
+[**MedPhyGraph/support-graph-data**](https://huggingface.co/datasets/MedPhyGraph/support-graph-data)
+
+The dataset release contains:
+
+- the frozen candidate-edge dataset and split
+- structured Procedural scene states
+- the 136-case Procedural subset of the expanded transfer evaluation
+
+NVIDIA Isaac for Healthcare assets and Isaac-derived structured scene states
+are not redistributed in that repository.
+
+## Scope
+
+CF-SupportNet is intended to reproduce and study the learned scoring component
+used in MedPhyGraph.
+
+The model does not directly provide:
+
+- scene perception
+- RGB/image understanding
+- object detection
+- complete physical simulation
+- the deterministic MedPhyGraph consistency modules
+- NVIDIA Isaac for Healthcare assets or scenes
+
+The complete MedPhyGraph method combines the learned scorer with deterministic
+graph-maintenance logic.
+
+## Integrity
+
+The repository includes:
+
+- **`checkpoint_manifest.json`** — metadata for the released checkpoints
+- **`SHA256SUMS.txt`** — SHA256 hashes for checkpoint integrity verification
+
+The primary seed-0 checkpoint has SHA256:
+
+```text
+e0b34529745399ecc5da5341ed7a162173611e12c8bd50dec121b0c575c5b789
+```
+
+## License
+
+The released CF-SupportNet model weights are provided under the
+**Creative Commons Attribution 4.0 International License (CC BY 4.0)**.
+
+Please provide appropriate attribution when using or redistributing these
+weights.
+
+The license for the MedPhyGraph source code is separate from the model-weight
+license.
 
 ## Citation
 
+If you use CF-SupportNet, MedPhyGraph, or these released checkpoints, please
+cite:
+
 ```bibtex
 @inproceedings{gholizadeh2026medphygraph,
-  title={MedPhyGraph: Counterfactual Support-Graph Maintenance for Dynamic Built-Environment Digital Twins},
-  author={Gholizadeh HamlAbadi, Kamran and Vahdati, Monica and El Saddik, Abdulmotaleb},
-  booktitle={TwinWorld: Visual Intelligence for Built Environment Digital Twins, ECCV 2026 Workshop},
-  year={2026}
+  title     = {MedPhyGraph: Counterfactual Support-Graph Maintenance for Dynamic Built-Environment Digital Twins},
+  author    = {Gholizadeh HamlAbadi, Kamran and Vahdati, Monica and El Saddik, Abdulmotaleb},
+  booktitle = {TwinWorld: Visual Intelligence for Built Environment Digital Twins, ECCV 2026 Workshop},
+  year      = {2026}
 }
-```
+```