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construction-tagger-thesis
alexha11/construction-tagger-thesis
construction-tagger-thesis is a machine learning model from alexha11. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as apache-2.0.
A LoRA adapter for Qwen2-VL-7B-Instruct fine-tuned to tag construction-site photographs with infrastructure labels.
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Public
Updated Aug 31, 2026
Repo size
160 MB
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At a glance
- License
- apache-2.0
- Access
- Public
- Created
- Aug 31, 2026
- Updated
- Aug 31, 2026
- SHA
- 6c3d5504
Base models
Model card
From the Hugging Face model README
Construction Site Tagger — Thesis Model
A LoRA adapter for Qwen2-VL-7B-Instruct fine-tuned to tag construction-site photographs with infrastructure labels.
This is the thesis model — the version evaluated and reported in the research study.
The production successor is alexha11/construction-tagger-soupR.
Performance
| Eval set | Score |
|---|---|
| Thesis frozen set (original labels) | 0.866 |
| Thesis reported accuracy | 85.2 % |
Tags recognised
shallow_trench · medium_trench · deep_trench · cable_protection ·
warning_tape · vegetated_ground · cable_drum · junction_box ·
manhole · telecom_duct · electricity_duct
Adapter details
| Parameter | Value |
|---|---|
| Rank (r) | 16 |
| Alpha | 32 |
| Target modules | q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj |
| Training images | 1 155 |
Usage
from transformers import AutoProcessor, Qwen2VLForConditionalGeneration
from peft import PeftModel
base = Qwen2VLForConditionalGeneration.from_pretrained(
"Qwen/Qwen2-VL-7B-Instruct", torch_dtype="auto", device_map="auto"
)
model = PeftModel.from_pretrained(base, "alexha11/construction-tagger-thesis")
processor = AutoProcessor.from_pretrained("alexha11/construction-tagger-thesis")
Live demo
- License
- apache-2.0
- Languages
- en
- Created
- Aug 31, 2026
- Updated
- Aug 31, 2026