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-41-_png.rf.03f24ae05c2a3ef6947b0b76c456aa3e.txt
| 2 0.21142578125 0.6293402777777778 0.06903076171875 0.07709418402777778 | |
| 1 0.9460565476190477 0.8161375661375663 0.10788690476190474 0.35449735449735437 | |
| 2 0.35791015625 0.5911458333333334 0.049041748046875 0.06624348958333333 | |
| 2 0.314453125 0.5199652777777778 0.0411376953125 0.049235026041666664 | |
| 2 0.384033203125 0.5056423611111112 0.035064697265625 0.04234483506944445 | |
| 2 0.5810546875 0.5438368055555556 0.04052734375 0.0632052951388889 | |
| 2 0.54931640625 0.4483506944444444 0.0236968994140625 0.033284505208333336 | |
| 2 0.42236328125 0.5360243055555556 0.037109375 0.059027777777777776 | |
| 2 0.408935546875 0.4505208333333333 0.0257568359375 0.03379991319444445 | |
| 2 0.383544921875 0.4390190972222222 0.0271148681640625 0.03366427951388889 | |
| 2 0.7099609375 0.6762152777777778 0.06256103515625 0.10015190972222222 | |
| 2 0.43115234375 0.4151475694444444 0.017852783203125 0.024997287326388888 | |
| 2 0.456787109375 0.4466145833333333 0.0209197998046875 0.03184678819444445 | |
| 2 0.56298828125 0.3825954861111111 0.0115509033203125 0.0164794921875 | |
| 2 0.73828125 0.9470486111111112 0.119384765625 0.09944661458333333 | |
| 2 0.469482421875 0.4032118055555556 0.017852783203125 0.032280815972222224 | |
| 2 0.443603515625 0.400390625 0.01337432861328125 0.021023220486111112 | |
| 2 0.427734375 0.390625 0.01146697998046875 0.017876519097222224 | |
| 2 0.47900390625 0.3773871527777778 0.010650634765625 0.016221788194444444 | |
| 2 0.455322265625 0.3765190972222222 0.0117340087890625 0.016805013020833332 | |