Image-Text-to-Text
PEFT
Safetensors
English
llava
lora
pavement-distress
structural-health-monitoring
multimodal
vision-language
computer-vision
nlp
conversational
Instructions to use AIdeveric/llava-pavement-distress-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use AIdeveric/llava-pavement-distress-lora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("llava-hf/llava-1.5-7b-hf") model = PeftModel.from_pretrained(base_model, "AIdeveric/llava-pavement-distress-lora") - Notebooks
- Google Colab
- Kaggle
π£οΈ LLaVA-1.5-7B LoRA Adapter - Pavement Distress Analysis
AI-powered pavement distress analysis using LLaVA-1.5-7B with LoRA fine-tuning.
This repository contains the LoRA adapter achieving 74.31% Entity F1 on pavement distress detection, severity assessment, and maintenance recommendation.
π Quick Start - Try the Demo!
π Download and run the Gradio demo notebook:
Download the notebook:
LLaVA_Structural_Health_Monitoring_Pavement_Distress_Analyzer.ipynbUpload to Google Colab:
- Go to Google Colab
- Click File β Upload notebook
- Select the downloaded
.ipynbfile
Run all cells:
- Click Runtime β Run all
- Wait 3-5 minutes for model to load
- Click the public Gradio link to start analyzing images!
Test with sample images:
- Download sample images from
/imagesfolder - Upload to the Gradio interface
- Get instant distress analysis!
- Download sample images from
β No setup required - runs entirely in the cloud!
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Model tree for AIdeveric/llava-pavement-distress-lora
Base model
llava-hf/llava-1.5-7b-hf