Object Detection
ultralytics
computer-vision
yolov8
vehicle-detection
traffic-analysis
highway-monitoring
Instructions to use vietnguyennn0705/highway-vehicle-detection-code with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use vietnguyennn0705/highway-vehicle-detection-code with ultralytics:
from ultralytics import YOLOvv8 model = YOLOvv8.from_pretrained("vietnguyennn0705/highway-vehicle-detection-code") source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
| labeldata - v2 2025-10-22 6:55pm | |
| ============================== | |
| This dataset was exported via roboflow.com on October 22, 2025 at 11:56 AM GMT | |
| Roboflow is an end-to-end computer vision platform that helps you | |
| * collaborate with your team on computer vision projects | |
| * collect & organize images | |
| * understand and search unstructured image data | |
| * annotate, and create datasets | |
| * export, train, and deploy computer vision models | |
| * use active learning to improve your dataset over time | |
| For state of the art Computer Vision training notebooks you can use with this dataset, | |
| visit https://github.com/roboflow/notebooks | |
| To find over 100k other datasets and pre-trained models, visit https://universe.roboflow.com | |
| The dataset includes 92 images. | |
| Objects are annotated in YOLOv8 format. | |
| The following pre-processing was applied to each image: | |
| * Auto-orientation of pixel data (with EXIF-orientation stripping) | |
| * Resize to 640x640 (Fit within) | |
| No image augmentation techniques were applied. | |