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
highway-vehicle-detection-code / finetune_dataset /labels /Screenshot-33-_png.rf.de1547915cf88eb3d45116a09ff2849a.txt
| 1 0.9036458333333334 0.8551587301587302 0.1927083333333332 0.2896825396825392 | |
| 7 0.4032738095238096 0.42724867724867754 0.050595238095238096 0.11904761904761914 | |
| 7 0.4694768851810238 0.35431374727437726 0.015079557339861615 0.03306332572295574 | |
| 2 0.4342912513880134 0.419546795322371 0.012063645871889328 0.024127291743778458 | |
| 2 0.42724609375 0.5386284722222222 0.0374755859375 0.05360243055555555 | |
| 2 0.45654296875 0.4479166666666667 0.0219879150390625 0.03233506944444445 | |
| 2 0.450439453125 0.3891059027777778 0.0115966796875 0.018364800347222224 | |
| 2 0.3642578125 0.8033854166666666 0.07904052734375 0.12337239583333333 | |
| 2 0.576171875 0.5182291666666666 0.034423828125 0.05360243055555555 | |
| 2 0.46240234375 0.4188368055555556 0.0171051025390625 0.022976345486111112 | |
| 1 0.287841796875 0.6019965277777778 0.132568359375 0.22200520833333334 | |
| 2 0.4716796875 0.3901909722222222 0.01422882080078125 0.021755642361111112 | |
| 2 0.53466796875 0.4242621527777778 0.0188751220703125 0.027737087673611112 | |
| 7 0.45166015625 0.3552517361111111 0.017852783203125 0.03439670138888889 | |
| 2 0.572265625 0.4262152777777778 0.017852783203125 0.026692708333333332 | |
| 2 0.055511474609375 0.8307291666666666 0.1087646484375 0.13509114583333334 | |
| 2 0.529296875 0.3947482638888889 0.0144500732421875 0.028157552083333332 | |
| 2 0.56396484375 0.380859375 0.01061248779296875 0.017320421006944444 | |