Transformers
Safetensors
surgical-video
spatio-temporal-grounding
medical-vision-language-model
eccv-2026
Instructions to use linzher/RefineRank with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use linzher/RefineRank with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("linzher/RefineRank", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Checkpoints
RefineRank 推理/训练所需的全部权重,平铺结构如下:
checkpoints/
├── vlm/ # 冻结的 MedVLM(Qwen2.5-VL 架构,HF 格式,全部文件直接放这里)
│ ├── config.json / model-0000X-of-00004.safetensors / tokenizer.json / ...
├── grounding_dino/ # 冻结的 GroundingDINO 权重
│ └── groundingdino_swinb_cogcoor.pth
└── refinenet/ # 唯一可训练模块 RefineNet(约 1.25M 参数)
├── proposal_adapter_full.pt # 随论文发布的最终提交权重
└── deployment_manifest.json
- 从 Hugging Face 一键恢复本目录:
hf download linzher/RefineRank --local-dir . vlm/与grounding_dino/在训练与推理中都保持冻结,仅用于特征与候选框提取。python interface.py predict不带--checkpoint时优先使用本目录的平铺权重; 训练新产生的run_<时间戳>/目录(python interface.py train的输出)也会被自动发现。- 大权重文件不要提交到 git。