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-51-_png.rf.a6d21d31a7bb92a9894e3c63b5f63d38.txt
| 1 0.283203125 0.5125868055555556 0.09185791015625 0.1400824652777778 | |
| 2 0.45263671875 0.453125 0.0230712890625 0.03271484375 | |
| 2 0.67822265625 0.8541666666666666 0.09686279296875 0.21158854166666666 | |
| 2 0.4130859375 0.44921875 0.02191162109375 0.03271484375 | |
| 2 0.4296875 0.5377604166666666 0.036712646484375 0.05259874131944445 | |
| 2 0.744140625 0.6015625 0.0567626953125 0.0773654513888889 | |
| 2 0.46923828125 0.40625 0.015777587890625 0.0218505859375 | |
| 2 0.5419921875 0.4500868055555556 0.021240234375 0.033935546875 | |
| 2 0.56982421875 0.5169270833333334 0.033477783203125 0.05577256944444445 | |
| 2 0.382568359375 0.443359375 0.0201416015625 0.028835720486111112 | |
| 2 0.6259765625 0.4622395833333333 0.023956298828125 0.03407118055555555 | |
| 2 0.61962890625 0.5108506944444444 0.034881591796875 0.04847547743055555 | |
| 2 0.431884765625 0.4171006944444444 0.0169830322265625 0.023328993055555556 | |
| 2 0.43798828125 0.3795572916666667 0.009674072265625 0.015787760416666668 | |
| 2 0.36376953125 0.7508680555555556 0.07598876953125 0.11729600694444445 | |
| 2 0.403076171875 0.4177517361111111 0.0177764892578125 0.02490234375 | |
| 2 0.476806640625 0.3821614583333333 0.01085662841796875 0.016737196180555556 | |
| 2 0.5703125 0.3938802083333333 0.01233673095703125 0.017591688368055556 | |
| 2 0.52734375 0.3841145833333333 0.01282501220703125 0.017130533854166668 | |
| 2 0.4599609375 0.3765190972222222 0.01111602783203125 0.0166015625 | |
| 2 0.58935546875 0.4123263888888889 0.0156707763671875 0.020222981770833332 | |
| 2 0.2340087890625 0.75 0.094482421875 0.11555989583333333 | |
| 2 0.44775390625 0.3708767361111111 0.00916290283203125 0.013678656684027778 | |
| 2 0.55224609375 0.3715277777777778 0.0101318359375 0.014784071180555556 | |
| 2 0.46630859375 0.3680555555555556 0.00937652587890625 0.016343858506944444 | |