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Model Details
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Thamed-Chowdhury
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Qwen/Qwen3-VL-8B-Instruct
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| Model / setting | Risk QWK | Exact-risk accuracy |
|---|---|---|
| Zero-shot base (Qwen3-VL-8B-Instruct) | 0.077 [0.044, 0.108] | 0.544 |
| EG-ARSA (this adapter) | 0.482 [0.454, 0.510] | 0.717 |
Fine-tuning lifts ordinal risk agreement by +0.40 QWK (non-overlapping bootstrap CIs).
Under a blind human-expert evaluation, EG-ARSA is risk-correct 81% of the time vs
58% for Gemini-2.5-flash and 42% for the 31B teacher run leakage-free; fully
automated risk accuracy reproduces the ranking (0.74 / 0.59 / 0.36). Per-class F1 (raw
operating point): Low 0.04 / Medium 0.67 / High 0.77 — residual errors are predominantly
between adjacent risk levels. See the reports/ folder and the paper for the full
evaluation, including the multi-model comparison and human-eval rubric.
python
import torchfrom transformers import Qwen3VLForConditionalGeneration, AutoProcessorfrom peft import PeftModelfrom PIL import Image BASE = "Qwen/Qwen3-VL-8B-Instruct"LORA = "Thamed-Chowdhury/eg-arsa-qwen3vl-8b-lora" proc = AutoProcessor.from_pretrained(BASE, trust_remote_code=True)model = Qwen3VLForConditionalGeneration.from_pretrained(BASE, dtype=torch.bfloat16, device_map="cuda")model = PeftModel.from_pretrained(model, LORA).eval() SYSTEM = ("You are a road-safety auditor applying the LGED (Local Government Engineering " "Department) 12-category visual audit methodology to road imagery in Bangladesh.")# The canonical leakage-free instruction is FULL_AUDIT_INSTRUCTION in# prompts/finetune_prompts.py (shipped with the dataset and the code repo).INSTRUCTION = "Audit this road image for safety hazards. Return the structured JSON audit." img = Image.open("road.jpg").convert("RGB")msgs = [ {"role": "system", "content": [{"type": "text", "text": SYSTEM}]}, {"role": "user", "content": [{"type": "image", "image": img}, {"type": "text", "text": INSTRUCTION}]},]text = proc.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True)inputs = proc(text=[text], images=[img], return_tensors="pt").to("cuda")out = model.generate(**inputs, max_new_tokens=1024, do_sample=False)print(proc.tokenizer.decode(out[0, inputs["input_ids"].shape[1]:], skip_special_tokens=True))For exact parity with the paper, use the canonical
FULL_AUDIT_INSTRUCTION(full road scene) /SINGLE_HAZARD_INSTRUCTIONfromprompts/finetune_prompts.pyand 1024-px native resolution. See the code repo for the wrapped inference helper (apps/streetview_infer.py) and the evaluation pipeline.
q/k/v/o_proj.tasks_available.Intended use. Proactive, low-cost screening of rural/suburban (LGED-class) road imagery to surface visible safety hazards and an interpretable risk rating where formal Road Safety Audits are unaffordable. It is a decision-support tool, not a replacement for a formal multidisciplinary RSA.
Limitations / out of scope.
skid_resistance (friction) and drainage (wet-weather behaviour) are recoverable only
in obvious cases. All 12 categories are reported; these two score lowest.The training supervision was produced via Expert-Grounded Distillation. Two points of provenance matter for downstream use:
The LoRA adapter weights and code in this repository are released under Apache-2.0.
The base model Qwen/Qwen3-VL-8B-Instruct is governed by its own license; only the adapter
is redistributed here. Because the model was distilled in part from Gemma-generated
supervision (see Data provenance & terms above), use of the model is also subject to
the Gemma Terms of Use and Prohibited Use Policy. The BD-ARSA dataset's annotations are
released under CC BY 4.0 (the street-view imagery is not redistributed).
Anyone who uses the EG-ARSA model or the BD-ARSA dataset — in research, in a product, or in any derivative dataset or model — must cite the paper:
Md Thamed Bin Zaman Chowdhury and Moazzem Hossain. EG-ARSA: An Expert-Grounded Open Model for Visual Road Safety Auditing in Low-Resource Settings. arXiv:2608.23563, 2026. https://arxiv.org/abs/2608.23563
bibtex
@article{chowdhury2026egarsa, title = {EG-ARSA: An Expert-Grounded Open Model for Visual Road Safety Auditing in Low-Resource Settings}, author = {Chowdhury, Md Thamed Bin Zaman and Hossain, Moazzem}, journal = {arXiv preprint arXiv:2608.23563}, year = {2026}, eprint = {2608.23563}, archivePrefix = {arXiv}, primaryClass = {cs.CV}, doi = {10.48550/arXiv.2608.23563}, url = {https://arxiv.org/abs/2608.23563}}Attribution is also a license condition for the BD-ARSA annotations, which are released under CC BY 4.0.
The expert ground truth derives from on-site Road Safety Audits conducted by faculty of the Accident Research Institute (ARI), Bangladesh University of Engineering and Technology (BUET), commissioned by the Local Government Engineering Department (LGED) under the World Bank–financed Second Rural Transport Improvement Project (RTIP-II, Additional Financing; P166295).
import torchfrom transformers import Qwen3VLForConditionalGeneration, AutoProcessorfrom peft import PeftModelfrom PIL import Image BASE = "Qwen/Qwen3-VL-8B-Instruct"LORA = "Thamed-Chowdhury/eg-arsa-qwen3vl-8b-lora" proc = AutoProcessor.from_pretrained(BASE, trust_remote_code=True)model = Qwen3VLForConditionalGeneration.from_pretrained(BASE, dtype=torch.bfloat16, device_map="cuda")model = PeftModel.from_pretrained(model, LORA).eval() SYSTEM = ("You are a road-safety auditor applying the LGED (Local Government Engineering " "Department) 12-category visual audit methodology to road imagery in Bangladesh.")# The canonical leakage-free instruction is FULL_AUDIT_INSTRUCTION in# prompts/finetune_prompts.py (shipped with the dataset and the code repo).INSTRUCTION = "Audit this road image for safety hazards. Return the structured JSON audit." img = Image.open("road.jpg").convert("RGB")msgs = [ {"role": "system", "content": [{"type": "text", "text": SYSTEM}]}, {"role": "user", "content": [{"type": "image", "image": img}, {"type": "text", "text": INSTRUCTION}]},]text = proc.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True)inputs = proc(text=[text], images=[img], return_tensors="pt").to("cuda")out = model.generate(**inputs, max_new_tokens=1024, do_sample=False)print(proc.tokenizer.decode(out[0, inputs["input_ids"].shape[1]:], skip_special_tokens=True))@article{chowdhury2026egarsa, title = {EG-ARSA: An Expert-Grounded Open Model for Visual Road Safety Auditing in Low-Resource Settings}, author = {Chowdhury, Md Thamed Bin Zaman and Hossain, Moazzem}, journal = {arXiv preprint arXiv:2608.23563}, year = {2026}, eprint = {2608.23563}, archivePrefix = {arXiv}, primaryClass = {cs.CV}, doi = {10.48550/arXiv.2608.23563}, url = {https://arxiv.org/abs/2608.23563}}