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-30-_png.rf.0176eb82a28f35de7319ed567d761aa6.txt
| 2 0.435546875 0.4108072916666667 0.0162811279296875 0.024712456597222224 | |
| 2 0.38623046875 0.6870659722222222 0.063720703125 0.09651692708333333 | |
| 2 0.59814453125 0.4752604166666667 0.0282745361328125 0.037353515625 | |
| 2 0.58203125 0.5538194444444444 0.038909912109375 0.06635199652777778 | |
| 2 0.69384765625 0.5368923611111112 0.039703369140625 0.054117838541666664 | |
| 2 0.53271484375 0.4040798611111111 0.0160980224609375 0.023871527777777776 | |
| 2 0.299072265625 0.6328125 0.064208984375 0.08946397569444445 | |
| 1 0.282958984375 0.5099826388888888 0.09930419921875 0.142578125 | |
| 2 0.38623046875 0.53515625 0.037200927734375 0.0576171875 | |
| 2 0.410400390625 0.4539930555555556 0.0253753662109375 0.032904730902777776 | |
| 2 0.5478515625 0.3869357638888889 0.01242828369140625 0.018649631076388888 | |
| 2 0.0585174560546875 0.80859375 0.117034912109375 0.13563368055555555 | |
| 2 0.544921875 0.4557291666666667 0.021453857421875 0.034857855902777776 | |
| 2 0.56396484375 0.4095052083333333 0.015777587890625 0.024156358506944444 | |
| 7 0.564453125 0.3634982638888889 0.01554107666015625 0.042670355902777776 | |
| 3 0.394287109375 0.4173177083333333 0.0244293212890625 0.042426215277777776 | |
| 7 0.451904296875 0.3778211805555556 0.0211181640625 0.04342990451388889 | |
| 2 0.428466796875 0.3878038194444444 0.01107025146484375 0.0179443359375 | |
| 2 0.478515625 0.3765190972222222 0.0101318359375 0.015665690104166668 | |
| 2 0.70458984375 0.9253472222222222 0.11676025390625 0.1449652777777778 | |
| 3 0.48974609375 0.3387586805555556 0.00974273681640625 0.022013346354166668 | |