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-47-_png.rf.c87e637f9d4177ef17b92974b3216571.txt
| 3 0.2938988095238096 0.6435185185185187 0.07738095238095237 0.12037037037037032 | |
| 7 0.34561011904761907 0.44841269841269854 0.07068452380952381 0.126984126984127 | |
| 2 0.3932291666666668 0.4794973544973549 0.026041666666666786 0.04365079365079383 | |
| 2 0.45126488095238093 0.43915343915343924 0.02157738095238102 0.034391534391534626 | |
| 2 0.533482142857143 0.41534391534391546 0.01636904761904745 0.021164021164021246 | |
| 2 0.472900390625 0.3878038194444444 0.01554107666015625 0.022528754340277776 | |
| 2 0.43408203125 0.4151475694444444 0.015716552734375 0.023776584201388888 | |
| 1 0.564453125 0.3997395833333333 0.0293731689453125 0.0671115451388889 | |
| 2 0.412841796875 0.4058159722222222 0.014007568359375 0.0203857421875 | |
| 2 0.5400390625 0.3756510416666667 0.01061248779296875 0.015313042534722222 | |
| 2 0.59765625 0.484375 0.027587890625 0.04188368055555555 | |
| 2 0.5390625 0.4077690972222222 0.016845703125 0.023871527777777776 | |
| 2 0.379150390625 0.7348090277777778 0.0753173828125 0.10921223958333333 | |
| 2 0.442138671875 0.3982204861111111 0.0144500732421875 0.022976345486111112 | |
| 2 0.4658203125 0.4136284722222222 0.017852783203125 0.021755642361111112 | |
| 2 0.521484375 0.3611111111111111 0.00861358642578125 0.014336480034722222 | |
| 2 0.43896484375 0.3752170138888889 0.00989532470703125 0.016099717881944444 | |
| 2 0.205810546875 0.6332465277777778 0.07159423828125 0.07958984375 | |
| 2 0.7568359375 0.6245659722222222 0.060760498046875 0.0849609375 | |