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QuickFixIt-model
RoxieRoller/QuickFixIt-model
QuickFixIt-model is a object detection model from RoxieRoller. Use it when you need objects located in an image. The card lists the license as mit.
Fine-tuned YOLOv8n and Multi-Class Road Defect Spatial Pyramid Classifier trained specifically for municipal road maintenance and automated repair verification in Indian urban environments.
Access
Public
Updated Sep 23, 2026
Repo size
7.1 MB
Likes
1
Public
Repo makeup
Click a slice to open those files.
.pt6.2 MB · 88%
At a glance
- Task
- Object Detection
- License
- mit
- Access
- Public
- Created
- Sep 20, 2026
- Updated
- Sep 23, 2026
- SHA
- 5f36c750
Model card
From the Hugging Face model README
🛣️ QuickFix It — AI Road Defect & Repair Verification Model
Fine-tuned YOLOv8n and Multi-Class Road Defect Spatial Pyramid Classifier trained specifically for municipal road maintenance and automated repair verification in Indian urban environments.
Developed as the core AI engine for QuickFix It (GitHub: mohammedhussain06/QuickFix).
🎯 Key Capabilities
-
4-Way Defect Differentiation:
- Potholes: Asphalt depressions, cavities, and broken paver rims.
- Manhole Collars: Circular/rectangular utility frames, seated covers, and hazardous open vertical shafts.
- Waterlogging: Carriageway flooding, standing water sheets, and poor drainage ponding.
- Cracks: Bituminous fractures, longitudinal tears, and alligator cracking patterns.
-
Automated Before/After Repair Auto-Verification:
- Anti-Substitution Defense (The Twist): Prevents contractors from gaming the system by photographing an already-fixed road from another location.
- Landmark Alignment: Validates background landmarks (e.g., Metro pillars, stationary vehicles, kerb lines) via SIFT/ORB homography invariance.
- Perceptual Integrity: Detects recycled photo fraud via pHash bitstream collision matching.
- GPS & Compass Verification: Validates repair camera coordinates (<15m) and heading tolerance (<25°).
📊 Model Information
| Parameter | Specification |
|---|---|
| Primary Architecture | YOLOv8n (Ultralytics) + 55-D Spatial Pyramid Forest |
| Model Weights | best.pt (~6.2 MB) |
| Feature Forest | trained_forest.json (Spatial Pyramid Texture & Gradient Ensembles) |
| Training Dataset | 2,281+ Indian road scenes (Roboflow Indian Pothole + municipal ground truth) |
| Precision / Recall | mAP50: 91.4%, Defect Classification Accuracy: 94.2% |
🚀 Quickstart Usage
1. Object Detection (YOLOv8)
from ultralytics import YOLO
from huggingface_hub import hf_hub_download
# Download weights from Hugging Face Hub
weights_path = hf_hub_download(repo_id="RoxieRoller/QuickFixIt-model", filename="best.pt")
# Load model & run inference
model = YOLO(weights_path)
results = model.predict(source="road_photo.jpg", conf=0.25)
results[0].show()
2. Full Verification Pipeline Integration
In your backend .env:
HF_MODEL_REPO=RoxieRoller/QuickFixIt-model
YOLO_MODEL_PATH=models/pothole_yolov8/weights/best.pt
👥 Authors & Repository
- Author: Mohammed Hussain (@mohammedhussain06)
- Platform Repo: github.com/mohammedhussain06/QuickFix
- Hugging Face Model: huggingface.co/RoxieRoller/QuickFixIt-model
- Task
- Object Detection
- License
- mit
- Created
- Sep 20, 2026
- Updated
- Sep 23, 2026