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-44-_png.rf.d9a77243dd30dcb860a7a01d643b76b7.txt
| 7 0.4676358247143401 0.3549783411505072 0.014755880336739757 0.03237223865837958 | |
| 1 0.86181640625 0.6710069444444444 0.217041015625 0.302734375 | |
| 2 0.410400390625 0.5733506944444444 0.04351806640625 0.052815755208333336 | |
| 2 0.436767578125 0.4986979166666667 0.03173828125 0.04299587673611111 | |
| 2 0.47021484375 0.3982204861111111 0.0141143798828125 0.019680447048611112 | |
| 2 0.204345703125 0.6510416666666666 0.0751953125 0.09233940972222222 | |
| 2 0.402099609375 0.4155815972222222 0.01507568359375 0.023966471354166668 | |
| 2 0.41943359375 0.3982204861111111 0.01302337646484375 0.01953125 | |
| 2 0.158203125 0.8849826388888888 0.122314453125 0.1584201388888889 | |
| 7 0.486572265625 0.33984375 0.0120086669921875 0.024522569444444444 | |
| 2 0.36474609375 0.4626736111111111 0.02880859375 0.036078559027777776 | |
| 2 0.411865234375 0.4437934027777778 0.02288818359375 0.029486762152777776 | |
| 2 0.42919921875 0.4155815972222222 0.0156707763671875 0.023966471354166668 | |
| 2 0.475830078125 0.3843315972222222 0.0117340087890625 0.019843207465277776 | |
| 2 0.456787109375 0.3836805555555556 0.01116180419921875 0.017320421006944444 | |
| 2 0.55322265625 0.3643663194444444 0.0117340087890625 0.014499240451388888 | |
| 7 0.5390625 0.34765625 0.012054443359375 0.033148871527777776 | |
| 2 0.444580078125 0.3741319444444444 0.01016998291015625 0.016289605034722224 | |