MUGWANEZAMANZI/Car-Phone-Violations

This notebook contains code of car phone violations tailored in Rwandan context with local datasets # Car-Phone Violations — Driver Distraction Detection (YOLOv8) This repository contains a YOLOv8-based object detection workflow for identifying distracted driving behaviors (e.g., phone use while driving). It includes the trained model artifact (`driver_model.pt`), a training/validation notebook, and dataset integration via Roboflow. - Researcher ID: orcid.org - Research lab: alabs.kwartisans.com ## Contents - `driver_model.pt`: Trained YOLOv8 model (detect) - `train_yolov8_object_detection_on_custom_dataset.ipynb`: End-to-end setup, training, validation, inference - `datasets/`: Roboflow-exported dataset structure (train/valid/test) ## Quick Start (Windows) 1. Create and activate a virtual environment: ```powershell python -m venv .venv .venv\Scripts\activate ``` 2. Install core packages: ```powershell pip install ultralytics==8.2.103 supervision ``` 3. Install Roboflow. If you encounter Windows file lock issues (WinError 5), either: - Preferred: ```powershell pip install requests-toolbelt pip install roboflow --no-deps ``` - Or standard (may update OpenCV dependencies): ```powershell pip install roboflow ``` ## Dataset The dataset is downloaded via Roboflow in the notebook and placed under `datasets/ /`. It follows the YOLOv8 format with `images/` and `labels/` for each split (`train/`, `valid/`, `test/`). To re-download programmatically in Python (with your API key and workspace/project/version): ```python from roboflow import Roboflow rf = Roboflow(api_key=" ") project = rf.workspace(" ").project(" ") version = project.version( ) dataset = version.download("yolov8") ``` ## Training Procedure Training was conducted with YOLOv8 using the following typical configuration (see the notebook for exact commands and any changes): - Base model: `yolov8s.pt` - Task: `detect` - Epochs: `50` - Image size: `800` - Plots: `True` CLI example (inside the notebook or terminal): ```powershell # from repository root # ensure dataset.loca …