Object Detection
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:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="linzher/RefineRank")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("linzher/RefineRank", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Remove root-level duplicate deployment_manifest.json; canonical copy under checkpoints/refinenet/run_iter132_submission/
Browse files- deployment_manifest.json +0 -17
deployment_manifest.json
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{
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"schema_version": 1,
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"decision": "PROPOSAL_REPAIR_FULL_FIT_COMPLETE",
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"model_kind": "proposal_repair_quality",
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"selector_kind": "repair_quality",
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"repair_variant": "full",
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"query_mode": "q_last",
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"decoder_mode": "fixed_current",
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"operator_authorized": true,
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"fit_protocol": "fixed_fold_selected_then_all_train_groups_full_fit",
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"feature_contract_hash": "7fd672157bfa0174e802e66d1c37b5dd7996214edb191f9fba0c926f73d38748",
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"proposal_checkpoint": "checkpoints/proposal_adapter_full.pt",
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"proposal_checkpoint_sha256": "932e479b854c3d5fbafee25a3fcf9e6481e864fe98a6867502d6ebff39463d9b",
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"frozen_inputs_unchanged": true,
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"official_leaderboard_claim": false,
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"release_note": "Camera-ready release manifest reconstructed from the checkpoint metadata (adapter_config, train_config, standardizer, source_feature_contract_hash). Original training run: runs/kept_wf10_final/iter132_submission in the SurgST-VLM research workspace."
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}
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