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-52-_png.rf.d7fa2cc24ee96d9fed76ce5f874ad600.txt
| 1 0.346435546875 0.4544270833333333 0.0552978515625 0.09288194444444445 | |
| 7 0.1356201171875 0.6475694444444444 0.26171875 0.3444010416666667 | |
| 3 0.2332589285714287 0.7599206349206352 0.10044642857142856 0.13624338624338622 | |
| 2 0.326171875 0.8841145833333334 0.1014404296875 0.1618923611111111 | |
| 2 0.34741210937500006 0.5568369708994709 0.04779052734375 0.060492621527777776 | |
| 2 0.42041015625 0.5651041666666666 0.040679931640625 0.06027560763888889 | |
| 2 0.448486328125 0.4774305555555556 0.026153564453125 0.03379991319444445 | |
| 2 0.461181640625 0.4233940972222222 0.01861572265625 0.025485568576388888 | |
| 2 0.431640625 0.41796875 0.017303466796875 0.024997287326388888 | |
| 2 0.5869140625 0.5963541666666666 0.047882080078125 0.08355034722222222 | |
| 2 0.5947265625 0.4251302083333333 0.017181396484375 0.023695203993055556 | |
| 2 0.40625 0.4151475694444444 0.014556884765625 0.022976345486111112 | |
| 2 0.75244140625 0.61328125 0.05792236328125 0.0823025173611111 | |
| 2 0.47509765625 0.3895399305555556 0.0147857666015625 0.019680447048611112 | |
| 2 0.42041015625 0.3980034722222222 0.01406097412109375 0.020453559027777776 | |
| 2 0.53662109375 0.4134114583333333 0.017303466796875 0.027316623263888888 | |
| 2 0.63037109375 0.4618055555555556 0.024932861328125 0.03420681423611111 | |
| 2 0.445068359375 0.3953993055555556 0.01332855224609375 0.019015842013888888 | |
| 2 0.44580078125 0.3697916666666667 0.009857177734375 0.01373291015625 | |
| 2 0.54150390625 0.369140625 0.01111602783203125 0.016289605034722224 | |
| 2 0.56005859375 0.3862847222222222 0.0141754150390625 0.024061414930555556 | |
| 2 0.468505859375 0.3661024305555556 0.0118255615234375 0.016343858506944444 | |
| 2 0.75146484375 0.9782986111111112 0.089111328125 0.04250759548611111 | |
| 2 0.5224609375 0.3595920138888889 0.0087127685546875 0.015733506944444444 | |