| # Multiple Car & Person Detection — YOLOv8x (Fine-Tuned) | |
| This repository provides a fine-tuned YOLOv8x model for real-time detection of multiple cars and persons in images and videos. | |
| It is designed for applications such as traffic monitoring, smart-city analytics, pedestrian awareness, and video surveillance. | |
| ## Key Features | |
| - Detects multiple objects per frame: | |
| - car | |
| - person | |
| - Real-time performance depending on hardware | |
| - High detection accuracy using YOLOv8x | |
| - Works on both images and videos | |
| - Built with Ultralytics YOLOv8 | |
| ## Model Details | |
| | Property | Value | | |
| |----------|-------| | |
| | Base Architecture | YOLOv8x | | |
| | Task | Object Detection | | |
| | Framework | Ultralytics | | |
| | Detected Classes | person, car | | |
| | Input | Images / Video | | |
| | Output | Bounding boxes + confidence scores | | |
| This model was fine-tuned on datasets containing vehicles and pedestrians in urban environments, optimized for scenarios where multiple vehicles and people appear in the same scene. | |
| ## Installation | |
| Install the required dependencies: | |
| ```bash | |
| pip install ultralytics opencv-python torch torchvision | |
| ``` | |
| ## Model Files | |
| Multiple_car_detection.pt.pt - Best model from training | |
| ## Example | |
|  | |
| ## Citation | |
| If you use this model, please cite: | |
| ```bibtex | |
| @misc{multiple-car-detection-2025, | |
| author = {Malek Messaoudi and Yassine Mhirsi}, | |
| title = {Multiple Car and Persons Detection}, | |
| year = {2025}, | |
| publisher = {Hugging Face}, | |
| } | |
| ``` | |
| ## license | |
| license: mit | |