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-17-_png.rf.2548677b072f2c9c4fd7176e823cd389.txt
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| 1 0.142822265625 0.6497395833333334 0.20947265625 0.2628038194444444 | |
| 2 0.398681640625 0.4735243055555556 0.0280609130859375 0.036214192708333336 | |
| 2 0.353271484375 0.5520833333333334 0.039642333984375 0.055257161458333336 | |
| 2 0.364013671875 0.4600694444444444 0.0260009765625 0.032470703125 | |
| 2 0.84375 0.7269965277777778 0.08587646484375 0.11881510416666667 | |
| 2 0.44189453125 0.4947916666666667 0.0295867919921875 0.04014756944444445 | |
| 2 0.40380859375 0.4136284722222222 0.017242431640625 0.022705078125 | |
| 2 0.412353515625 0.5963541666666666 0.053192138671875 0.0849609375 | |
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| 2 0.449951171875 0.3932291666666667 0.01262664794921875 0.018785264756944444 | |
| 2 0.338134765625 0.4930555555555556 0.03533935546875 0.044596354166666664 | |
| 3 0.470947265625 0.3986545138888889 0.016021728515625 0.02490234375 | |
| 2 0.56103515625 0.412109375 0.016021728515625 0.023776584201388888 | |
| 2 0.42431640625 0.3934461805555556 0.015716552734375 0.020779079861111112 | |
| 2 0.62646484375 0.4587673611111111 0.0244293212890625 0.03586154513888889 | |
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| 2 0.53515625 0.3522135416666667 0.00766754150390625 0.009874131944444444 | |
| 2 0.54248046875 0.3470052083333333 0.006389617919921875 0.011271158854166666 | |
| 2 0.52294921875 0.3526475694444444 0.006984710693359375 0.010715060763888888 | |
| 2 0.433349609375 0.3875868055555556 0.009857177734375 0.0150146484375 | |
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