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-34-_png.rf.e29601947bcc60ea2c794a7ac00359c5.txt
| 7 0.42857142857142866 0.39682539682539697 0.03422619047619051 0.08465608465608479 | |
| 1 0.37451171875 0.48046875 0.06280517578125 0.11328125 | |
| 7 0.473388671875 0.3480902777777778 0.0171051025390625 0.031141493055555556 | |
| 2 0.468017578125 0.4134114583333333 0.0160980224609375 0.024617513020833332 | |
| 2 0.449951171875 0.45703125 0.0240936279296875 0.032524956597222224 | |
| 2 0.434814453125 0.5342881944444444 0.033538818359375 0.04885525173611111 | |
| 2 0.544921875 0.4526909722222222 0.0228424072265625 0.035725911458333336 | |
| 2 0.26806640625 0.5655381944444444 0.050140380859375 0.056098090277777776 | |
| 2 0.381591796875 0.6072048611111112 0.053192138671875 0.07275390625 | |
| 2 0.5615234375 0.5234375 0.033905029296875 0.054009331597222224 | |
| 2 0.469482421875 0.396484375 0.01507568359375 0.021430121527777776 | |
| 2 0.064666748046875 0.8159722222222222 0.12933349609375 0.1312934027777778 | |
| 2 0.54052734375 0.4270833333333333 0.01666259765625 0.027940538194444444 | |
| 2 0.479248046875 0.375 0.01090240478515625 0.018500434027777776 | |
| 2 0.490966796875 0.3556857638888889 0.0074310302734375 0.011488172743055556 | |
| 2 0.634765625 0.5325520833333334 0.03662109375 0.054850260416666664 | |
| 2 0.587890625 0.4136284722222222 0.01763916015625 0.026285807291666668 | |
| 2 0.459716796875 0.3773871527777778 0.01009368896484375 0.015665690104166668 | |