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README.md

@@ -30,20 +30,22 @@ returns the highest-scoring original or refined box.
 ## Contents
 
 This repo hosts the complete `checkpoints/` tree expected by the code
-repository:
+repository — three flat folders, each holding its core files directly:
 
 ```
 checkpoints/
-├── vlm/uAI-NEXUS-MedVLM-1.0a-7B-RL/        # frozen MedVLM, HF format (~16 GB)
+├── vlm/                                  # frozen MedVLM, HF format (~16 GB)
+│   ├── config.json, generation_config.json, tokenizer*, preprocessor_config.json, ...
+│   └── model-00001..00004-of-00004.safetensors
 ├── grounding_dino/
-│   └── groundingdino_swinb_cogcoor.pth     # frozen GroundingDINO SwinB (~895 MB)
-└── refinenet/run_iter132_submission/       # trained RefineNet, this work (~5 MB)
-    ├── checkpoints/proposal_adapter_full.pt
+│   └── groundingdino_swinb_cogcoor.pth   # frozen GroundingDINO SwinB (~895 MB)
+└── refinenet/                            # trained RefineNet, this work (~5 MB)
+    ├── proposal_adapter_full.pt          # SHA-256: 932e479b…463d9b
     └── deployment_manifest.json
 ```
 
-- `refinenet/run_iter132_submission/` is the exact checkpoint behind the
-  paper's MedVidBench submission. `proposal_adapter_full.pt` SHA-256:
+- `refinenet/proposal_adapter_full.pt` is the exact checkpoint behind the
+  paper's MedVidBench submission (`run_iter132_submission`). SHA-256:
   `932e479b854c3d5fbafee25a3fcf9e6481e864fe98a6867502d6ebff39463d9b`.
 - `vlm/` and `grounding_dino/` are **third-party frozen weights**
   ([uAI-NEXUS-MedVLM](https://huggingface.co/UII-AI) by UII-AI and
@@ -62,7 +64,7 @@ git clone https://github.com/linzhe001/RefineRank
 cd RefineRank
 pip install -r requirements.txt
 # place the downloaded checkpoints/ next to interface.py, then:
-python interface.py predict   # auto-discovers checkpoints/refinenet/run_*
+python interface.py predict   # auto-discovers checkpoints/refinenet/
 ```
 
 ## Citation