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ronnief1
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Model Details
Model Provider
ronnief1
Model Tree
Qwen/Qwen3-VL-8B-Instruct
Input Modalities
Output Modalities
Supported Functionality
On the RetinaGroundBench visual-grounding benchmark (13 biomarker classes, AggIoU with t₁=0.1, t₂=0.3), RetGrounder reaches a macro F1 of 33.9% (AggIoU 18.5%, precision 48.0%, recall 27.5%) — a 43% relative improvement in F1 over the best general-purpose model evaluated (InternVL3.5-30B-A3B, F1 23.7%), despite its smaller size.
python
from transformers import Qwen3VLForConditionalGeneration, AutoProcessorfrom PIL import Image model = Qwen3VLForConditionalGeneration.from_pretrained( "ronnief1/RetGrounder", dtype="auto", device_map="auto")processor = AutoProcessor.from_pretrained("ronnief1/RetGrounder") image = Image.open("oct_bscan.jpg").convert("RGB")prompt = ('Locate every instance that belongs to the following categories: ' '"intraretinal fluid, drusen". Report bbox coordinates in JSON format.')messages = [{"role": "user", "content": [ {"type": "image", "image": image}, {"type": "text", "text": prompt}]}]inputs = processor.apply_chat_template( messages, tokenize=True, add_generation_prompt=True, return_dict=True, return_tensors="pt").to(model.device)out = model.generate(**inputs, max_new_tokens=1024)print(processor.batch_decode( [o[len(i):] for i, o in zip(inputs.input_ids, out)], skip_special_tokens=True)[0])Predicted boxes are in a 0–1000 normalized coordinate frame (scale back to pixels with the image width/height).
The RetGrounder weights are released under Apache-2.0, inherited from the base model Qwen/Qwen3-VL-8B-Instruct (also Apache-2.0). The RetinaGroundBench annotations are released separately under CC BY 4.0, and the underlying B-scans derive from the public OCTDL and OLIVES datasets — please also observe the terms of those original datasets.
If you use RetGrounder or RetinaGroundBench, please cite the paper:
bibtex
@misc{fecso2026visualgroundingbenchmarkretinal, title={A Visual Grounding Benchmark for Retinal OCT with Multimodal Large Language Models}, author={Ronald Fecso and José Morano and Botond Fazekas and Paul Widmann-Sedlnitzky and Dorota Himel and Laetita Hinterhuber and Virginia Mares and Andreas Pollreisz and Hrvoje Bogunović}, year={2026}, eprint={2610.31205}, archivePrefix={arXiv}, primaryClass={cs.CV}, url={https://arxiv.org/abs/2610.31205}, }from transformers import Qwen3VLForConditionalGeneration, AutoProcessorfrom PIL import Image model = Qwen3VLForConditionalGeneration.from_pretrained( "ronnief1/RetGrounder", dtype="auto", device_map="auto")processor = AutoProcessor.from_pretrained("ronnief1/RetGrounder") image = Image.open("oct_bscan.jpg").convert("RGB")prompt = ('Locate every instance that belongs to the following categories: ' '"intraretinal fluid, drusen". Report bbox coordinates in JSON format.')messages = [{"role": "user", "content": [ {"type": "image", "image": image}, {"type": "text", "text": prompt}]}]inputs = processor.apply_chat_template( messages, tokenize=True, add_generation_prompt=True, return_dict=True, return_tensors="pt").to(model.device)out = model.generate(**inputs, max_new_tokens=1024)print(processor.batch_decode( [o[len(i):] for i, o in zip(inputs.input_ids, out)], skip_special_tokens=True)[0])