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-45-_png.rf.7b96930ebc246460b0ea39cc660c269d.txt
| 2 0.27343750000000006 0.6884920634920636 0.07514880952380949 0.09656084656084647 | |
| 2 0.4144345238095239 0.5707671957671961 0.03720238095238089 0.07010582010582012 | |
| 1 0.5792410714285715 0.38888888888888895 0.02157738095238102 0.047619047619047526 | |
| 7 0.45424107142857145 0.3531746031746033 0.015625 0.03174603174603171 | |
| 2 0.4587053571428573 0.4252645502645505 0.01860119047619051 0.027777777777777776 | |
| 2 0.4395235144575985 0.406013696885021 0.013017448348030314 0.021889351804426208 | |
| 2 0.466796875 0.40625 0.01495361328125 0.021674262152777776 | |
| 2 0.457763671875 0.3776041666666667 0.012054443359375 0.016533745659722224 | |
| 2 0.475341796875 0.3851996527777778 0.0121002197265625 0.017591688368055556 | |
| 2 0.447509765625 0.3713107638888889 0.00974273681640625 0.016343858506944444 | |
| 2 0.4384765625 0.380859375 0.01078033447265625 0.016669379340277776 | |
| 2 0.443115234375 0.4722222222222222 0.027435302734375 0.036702473958333336 | |
| 2 0.52783203125 0.3971354166666667 0.01348114013671875 0.022013346354166668 | |
| 2 0.57568359375 0.4383680555555556 0.021331787109375 0.032958984375 | |
| 2 0.404052734375 0.4674479166666667 0.0280609130859375 0.041069878472222224 | |
| 2 0.53955078125 0.3680555555555556 0.00905609130859375 0.01373291015625 | |
| 2 0.483642578125 0.3643663194444444 0.0092315673828125 0.013468424479166666 | |
| 2 0.40185546875 0.41796875 0.0153656005859375 0.023871527777777776 | |
| 2 0.3134765625 0.5091145833333334 0.03985595703125 0.051025390625 | |
| 2 0.49365234375 0.3515625 0.009124755859375 0.0130615234375 | |
| 2 0.6123046875 0.4398871527777778 0.021820068359375 0.033284505208333336 | |
| 2 0.3779296875 0.4440104166666667 0.0244293212890625 0.030409071180555556 | |
| 2 0.55615234375 0.4735243055555556 0.0273284912109375 0.040608723958333336 | |
| 2 0.47021484375 0.3621961805555556 0.0089874267578125 0.013007269965277778 | |
| 2 0.2379150390625 0.5963541666666666 0.06768798828125 0.06787109375 | |
| 2 0.53125 0.3532986111111111 0.006412506103515625 0.012661404079861112 | |
| 2 0.5517578125 0.3650173611111111 0.006771087646484375 0.011854383680555556 | |
| 2 0.445556640625 0.3932291666666667 0.0116424560546875 0.015787760416666668 | |
| 2 0.544921875 0.3526475694444444 0.0082550048828125 0.01275634765625 | |
| 2 0.52880859375 0.3860677083333333 0.0083160400390625 0.013163248697916666 | |
| 2 0.5439453125 0.3305121527777778 0.0062408447265625 0.009955512152777778 | |