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-48-_png.rf.4fcbacdcfba644bdc9dec36cef958df4.txt
| 7 0.392333984375 0.4084201388888889 0.0455322265625 0.08338758680555555 | |
| 2 0.4241071428571429 0.42857142857142877 0.017857142857142884 0.034391534391534306 | |
| 3 0.385009765625 0.4956597222222222 0.035675048828125 0.05881076388888889 | |
| 2 0.43603515625 0.5208333333333334 0.036773681640625 0.051025390625 | |
| 2 0.5546875 0.4055989583333333 0.0153656005859375 0.023410373263888888 | |
| 2 0.328857421875 0.5915798611111112 0.055511474609375 0.078125 | |
| 2 0.44970703125 0.3923611111111111 0.01214599609375 0.017388237847222224 | |
| 2 0.1796875 0.6618923611111112 0.080810546875 0.09065755208333333 | |
| 1 0.62939453125 0.4904513888888889 0.077392578125 0.15104166666666666 | |
| 2 0.431884765625 0.38671875 0.01049041748046875 0.017591688368055556 | |
| 2 0.5234375 0.3784722222222222 0.011505126953125 0.015977647569444444 | |
| 2 0.56689453125 0.5013020833333334 0.0293731689453125 0.04820421006944445 | |
| 2 0.330810546875 0.4939236111111111 0.03338623046875 0.04147677951388889 | |
| 2 0.56640625 0.3825954861111111 0.01009368896484375 0.018147786458333332 | |
| 2 0.474853515625 0.388671875 0.01242828369140625 0.018500434027777776 | |
| 2 0.347900390625 0.8463541666666666 0.09222412109375 0.1457248263888889 | |
| 2 0.464111328125 0.4055989583333333 0.016021728515625 0.027859157986111112 | |
| 2 0.5927734375 0.4216579861111111 0.0135345458984375 0.023057725694444444 | |
| 2 0.53857421875 0.3634982638888889 0.0083465576171875 0.013522677951388888 | |
| 2 0.5693359375 0.5451388888888888 0.03533935546875 0.05995008680555555 | |