# arpy8/pothole_detection_yolov8 architecture

> Architecture diagram of the GitHub repository arpy8/pothole_detection_yolov8, generated by GitDiagram from its file tree, README and a sample of source files (last updated 2026-11-10). It is an AI-made overview: check details against the source.

- Interactive diagram: https://gitdiagram.com/arpy8/pothole_detection_yolov8
- Repository: https://github.com/arpy8/pothole_detection_yolov8

## Overview

This repository is a YOLOv8 pothole-detection model project. Its documented workflow takes annotated road imagery through preprocessing and training, evaluates and tunes the model, then uses the trained model to detect and localize potholes in images or videos. The notebook is the only source file listed, but no notebook excerpts were provided; therefore, the graph reflects README-documented stages and relationships rather than verified implementation details.

Main entry points: [`Pothole_Detection.ipynb`](https://github.com/arpy8/pothole_detection_yolov8/blob/main/Pothole_Detection.ipynb) (Model training)

## Diagram (Mermaid)

```mermaid
flowchart TD

subgraph group_data["Data preparation"]
  node_dataset["Annotated road data"]
  node_preprocessing["Preprocessing"]
end

subgraph group_model["Model workflow"]
  node_coco["COCO pretrained weights"]
  node_yolov8["YOLOv8 architecture"]
  node_training["Model training"]
  node_tuning["Hyperparameter tuning"]
  node_evaluation["Model evaluation"]
end

subgraph group_inference["Inference"]
  node_trained["Trained model"]
  node_inference["Image/video inference"]
  node_detections["Pothole detections"]
end

node_maintainer(("Model user"))

node_dataset -->|"preprocesses"| node_preprocessing
node_preprocessing -->|"feeds training"| node_training
node_coco -->|"provides weights"| node_yolov8
node_yolov8 -->|"initializes"| node_training
node_training -->|"produces"| node_trained
node_tuning -->|"tunes parameters"| node_training
node_trained -->|"is evaluated"| node_evaluation
node_trained -->|"runs on inputs"| node_inference
node_maintainer -->|"provides input"| node_inference
node_inference -->|"outputs boxes and scores"| node_detections

click node_preprocessing "https://github.com/arpy8/pothole_detection_yolov8/blob/main/Pothole_Detection.ipynb"
click node_training "https://github.com/arpy8/pothole_detection_yolov8/blob/main/Pothole_Detection.ipynb"
click node_tuning "https://github.com/arpy8/pothole_detection_yolov8/blob/main/Pothole_Detection.ipynb"
click node_evaluation "https://github.com/arpy8/pothole_detection_yolov8/blob/main/Pothole_Detection.ipynb"
click node_trained "https://github.com/arpy8/pothole_detection_yolov8/blob/main/Pothole_Detection.ipynb"
click node_inference "https://github.com/arpy8/pothole_detection_yolov8/blob/main/Pothole_Detection.ipynb"
click node_detections "https://github.com/arpy8/pothole_detection_yolov8/blob/main/Pothole_Detection.ipynb"

classDef toneNeutral fill:#f8fafc,stroke:#334155,stroke-width:1.5px,color:#0f172a
classDef toneBlue fill:#dbeafe,stroke:#2563eb,stroke-width:1.5px,color:#172554
classDef toneAmber fill:#fef3c7,stroke:#d97706,stroke-width:1.5px,color:#78350f
classDef toneMint fill:#dcfce7,stroke:#16a34a,stroke-width:1.5px,color:#14532d
classDef toneRose fill:#ffe4e6,stroke:#e11d48,stroke-width:1.5px,color:#881337
classDef toneIndigo fill:#e0e7ff,stroke:#4f46e5,stroke-width:1.5px,color:#312e81
classDef toneTeal fill:#ccfbf1,stroke:#0f766e,stroke-width:1.5px,color:#134e4a
class node_dataset,node_preprocessing,node_maintainer toneBlue
class node_coco,node_yolov8,node_training,node_tuning,node_evaluation toneAmber
class node_trained,node_inference,node_detections toneMint
```

## Components

### Data preparation

- **Annotated road data**
- [Preprocessing](https://github.com/arpy8/pothole_detection_yolov8/blob/main/Pothole_Detection.ipynb): `Pothole_Detection.ipynb`

### Model workflow

- **COCO pretrained weights**
- **YOLOv8 architecture**
- [Model training](https://github.com/arpy8/pothole_detection_yolov8/blob/main/Pothole_Detection.ipynb): `Pothole_Detection.ipynb`
- [Hyperparameter tuning](https://github.com/arpy8/pothole_detection_yolov8/blob/main/Pothole_Detection.ipynb): `Pothole_Detection.ipynb`
- [Model evaluation](https://github.com/arpy8/pothole_detection_yolov8/blob/main/Pothole_Detection.ipynb): `Pothole_Detection.ipynb`

### Inference

- [Trained model](https://github.com/arpy8/pothole_detection_yolov8/blob/main/Pothole_Detection.ipynb): `Pothole_Detection.ipynb`
- [Image/video inference](https://github.com/arpy8/pothole_detection_yolov8/blob/main/Pothole_Detection.ipynb): `Pothole_Detection.ipynb`
- [Pothole detections](https://github.com/arpy8/pothole_detection_yolov8/blob/main/Pothole_Detection.ipynb): `Pothole_Detection.ipynb`

### Other components

- **Model user**

## Connections

- Annotated road data → Preprocessing: preprocesses (evidence: `README.md`)
- Preprocessing → Model training: feeds training (evidence: `README.md`)
- COCO pretrained weights → YOLOv8 architecture: provides weights (evidence: `README.md`)
- YOLOv8 architecture → Model training: initializes (evidence: `README.md`)
- Model training → Trained model: produces (evidence: `README.md`)
- Hyperparameter tuning → Model training: tunes parameters (evidence: `README.md`)
- Trained model → Model evaluation: is evaluated (evidence: `README.md`)
- Trained model → Image/video inference: runs on inputs (evidence: `README.md`)
- Model user → Image/video inference: provides input (evidence: `README.md`)
- Image/video inference → Pothole detections: outputs boxes and scores (evidence: `README.md`)