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-15-_png.rf.3ca29d331ca9a7b5e151a140a8b98890.txt
| 2 0.463623046875 0.4210069444444444 0.018768310546875 0.027737087673611112 | |
| 3 0.44775390625 0.3825954861111111 0.02001953125 0.03379991319444445 | |
| 2 0.395751953125 0.48046875 0.0295867919921875 0.037272135416666664 | |
| 2 0.54345703125 0.4279513888888889 0.021453857421875 0.03379991319444445 | |
| 2 0.4482421875 0.4704861111111111 0.024139404296875 0.03420681423611111 | |
| 1 0.33984375 0.46484375 0.06982421875 0.10899522569444445 | |
| 2 0.57470703125 0.5529513888888888 0.039642333984375 0.07752821180555555 | |
| 2 0.20068359375 0.6605902777777778 0.08062744140625 0.08799913194444445 | |
| 2 0.4208984375 0.4353298611111111 0.0220794677734375 0.029378255208333332 | |
| 2 0.63818359375 0.5503472222222222 0.03875732421875 0.059624565972222224 | |
| 2 0.4423828125 0.4040798611111111 0.01666259765625 0.024237738715277776 | |
| 7 0.466064453125 0.3567708333333333 0.0141754150390625 0.025200737847222224 | |
| 1 0.389404296875 0.4114583333333333 0.039794921875 0.07438151041666667 | |
| 2 0.4755859375 0.3851996527777778 0.015838623046875 0.023328993055555556 | |
| 2 0.3623046875 0.5707465277777778 0.05645751953125 0.0857204861111111 | |
| 2 0.5712890625 0.3884548611111111 0.01337432861328125 0.017266167534722224 | |
| 2 0.462158203125 0.3756510416666667 0.0120086669921875 0.017198350694444444 | |
| 2 0.5244140625 0.3678385416666667 0.01107025146484375 0.0150146484375 | |
| 7 0.60400390625 0.4064670138888889 0.039642333984375 0.0783962673611111 | |