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-32-_png.rf.8ba72ef5605cdc0db2c705774d99626b.txt
| 7 0.46299070848866164 0.36010868156470516 0.017639147996915662 0.043833109814944156 | |
| 2 0.59619140625 0.6050347222222222 0.047149658203125 0.07915581597222222 | |
| 2 0.4140625 0.4505208333333333 0.0215301513671875 0.030409071180555556 | |
| 2 0.369384765625 0.7452256944444444 0.07354736328125 0.11284722222222222 | |
| 7 0.365234375 0.4769965277777778 0.08270263671875 0.1704644097222222 | |
| 2 0.440185546875 0.4049479166666667 0.01242828369140625 0.021185980902777776 | |
| 2 0.406005859375 0.4134114583333333 0.01262664794921875 0.022976345486111112 | |
| 2 0.46337890625 0.4084201388888889 0.0165863037109375 0.025580512152777776 | |
| 2 0.44091796875 0.5052083333333334 0.0309600830078125 0.046223958333333336 | |
| 2 0.4541015625 0.4500868055555556 0.0221099853515625 0.029269748263888888 | |
| 1 0.64453125 0.5221354166666666 0.08038330078125 0.1570095486111111 | |
| 2 0.55126953125 0.3917100694444444 0.0117340087890625 0.019843207465277776 | |
| 2 0.5439453125 0.4262152777777778 0.0182952880859375 0.027425130208333332 | |
| 2 0.48095703125 0.3700086805555556 0.00933837890625 0.014729817708333334 | |
| 2 0.5263671875 0.3893229166666667 0.0141754150390625 0.022447374131944444 | |
| 1 0.439697265625 0.3626302083333333 0.0165252685546875 0.039388020833333336 | |
| 2 0.9788818359375 0.9166666666666666 0.042236328125 0.1665581597222222 | |
| 2 0.55322265625 0.3661024305555556 0.00864410400390625 0.014444986979166666 | |
| 2 0.490966796875 0.3563368055555556 0.0082855224609375 0.014607747395833334 | |
| 7 0.47216796875 0.3407118055555556 0.01233673095703125 0.024617513020833332 | |
| 1 0.1077880859375 0.8372395833333334 0.21337890625 0.3248697916666667 | |