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-53-_png.rf.d5affce98068e11b6a393e82340493d2.txt
| 3 0.3422619047619048 0.5681216931216935 0.047619047619047714 0.07275132275132264 | |
| 7 0.2842261904761905 0.5006613756613758 0.11755952380952381 0.1838624338624339 | |
| 2 0.406494140625 0.6124131944444444 0.04949951171875 0.0773654513888889 | |
| 2 0.64501953125 0.4891493055555556 0.03173828125 0.04299587673611111 | |
| 2 0.39208984375 0.4822048611111111 0.03192138671875 0.039008246527777776 | |
| 2 0.5615234375 0.4895833333333333 0.029876708984375 0.04554578993055555 | |
| 2 0.440673828125 0.4908854166666667 0.0295867919921875 0.04234483506944445 | |
| 2 0.7470703125 0.6015625 0.05718994140625 0.07194010416666667 | |
| 2 0.455078125 0.4392361111111111 0.02069091796875 0.028157552083333332 | |
| 2 0.1754150390625 0.8472222222222222 0.10723876953125 0.13563368055555555 | |
| 2 0.468505859375 0.4032118055555556 0.014556884765625 0.0220947265625 | |
| 2 0.051544189453125 0.8472222222222222 0.10107421875 0.1472439236111111 | |
| 2 0.5732421875 0.4214409722222222 0.018157958984375 0.026082356770833332 | |
| 2 0.444091796875 0.3973524305555556 0.0137939453125 0.021185980902777776 | |
| 2 0.4228515625 0.3953993055555556 0.0115509033203125 0.018649631076388888 | |
| 2 0.43310546875 0.3845486111111111 0.01142120361328125 0.017130533854166668 | |
| 2 0.48046875 0.3767361111111111 0.011505126953125 0.016031901041666668 | |
| 2 0.54931640625 0.3895399305555556 0.011505126953125 0.017320421006944444 | |
| 2 0.4560546875 0.3804253472222222 0.01137542724609375 0.016343858506944444 | |
| 2 0.5244140625 0.3758680555555556 0.0108184814453125 0.014960394965277778 | |
| 1 0.3798828125 0.4205729166666667 0.039642333984375 0.06787109375 | |
| 2 0.54296875 0.376953125 0.0106964111328125 0.014892578125 | |