---
license: agpl-3.0
tags:
- yolov11
- yolov8
- yolo
- instance-segmentation
- object-detection
- vehicle-inspection
- car-damage
- ultralytics
- onnx
pipeline_tag: object-detection
base_model:
- Ultralytics/YOLO11
---

# ColCar Vision — Vehicle Inspection Models

This repository hosts the three vision models that power **ColCar (Car-Inspector)**,
an agent that assists with vehicle inspections, feeding a Colombian RTM
compliance RAG and pricing engine.

All three models are built with [Ultralytics YOLO](https://github.com/ultralytics/ultralytics)
(YOLOv11 and YOLOv8). Full credit to Ultralytics for the open YOLO
architecture and training tooling.

## Models

### 1. Damage Segmentation (YOLOv11-medium, instance segmentation)
`colcar-damage-seg.{pt,onnx}`
- **Classes:** crack, scratch, dent, glass_shatter, lamp_broken, tire_flat
- **Training data:** [Car-Damage-Type-Detection-End-Game](https://universe.roboflow.com/landebeau7/car-damage-type-detection-end-game-ranzq),
  Roboflow Universe, CC BY 4.0

### 2. Car Parts Segmentation (YOLOv11-medium, instance segmentation)
`colcar-parts-seg.{pt,onnx}`
- **Classes (21):** wheel, hood, trunk, bumpers, doors, glass, lights,
  mirrors, tailgate, etc.
- **Training data:** [car-seg](https://universe.roboflow.com/gianmarco-russo-vt9xr/car-seg-un1pm)
  from https://docs.ultralytics.com/datasets/segment/carparts-seg
  by **Gianmarco Russo**, Roboflow Universe, CC BY 4.0

### 3. Tyre Defect Detection (YOLOv8-medium, object detection)
`colcar-tyre-defect-det.{pt,onnx}`
- **Classes:** Good, Bulge, Cracks, Flat spots, Pitting, Puncture
- **Training data:** [Tyre defect detection](https://universe.roboflow.com/landebeau7/tyre-defect-detection-ewz6t),
  Roboflow Universe, CC BY 4.0

## Usage

### ONNX Runtime (inference only)
\`\`\`python
import onnxruntime as ort

session = ort.InferenceSession("colcar-damage-seg.onnx")
outputs = session.run(None, {"images": input_tensor})
\`\`\`

### Ultralytics (.pt — inference or further training)
\`\`\`python
from ultralytics import YOLO

model = YOLO("colcar-damage-seg.pt")
results = model.predict("car_photo.jpg")
\`\`\`

## License

Released under **AGPL-3.0**, in line with the Ultralytics YOLO license these
models were trained with (no Enterprise license was purchased).

## Related

- Project repo: `https://github.com/BeauBryanDev/colcar`