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-55-_png.rf.01045c744a4928cbc354b4e0e6ca2e33.txt
| 7 0.31689453125 0.4683159722222222 0.089599609375 0.15071614583333334 | |
| 2 0.5771484375 0.4344618055555556 0.020172119140625 0.028944227430555556 | |
| 2 0.556640625 0.4917534722222222 0.0297088623046875 0.044596354166666664 | |
| 2 0.31298828125 0.6037326388888888 0.056854248046875 0.07481553819444445 | |
| 2 0.56201171875 0.3977864583333333 0.0154876708984375 0.022976345486111112 | |
| 2 0.4609375 0.416015625 0.016845703125 0.023776584201388888 | |
| 2 0.443603515625 0.4800347222222222 0.0271148681640625 0.040228949652777776 | |
| 2 0.3701171875 0.4548611111111111 0.022674560546875 0.031982421875 | |
| 2 0.53857421875 0.4255642361111111 0.017913818359375 0.032470703125 | |
| 2 0.1556396484375 0.9097222222222222 0.1439208984375 0.1750217013888889 | |
| 2 0.431884765625 0.416015625 0.01751708984375 0.023966471354166668 | |
| 2 0.08154296875 0.8025173611111112 0.11798095703125 0.1300998263888889 | |
| 2 0.40576171875 0.4561631944444444 0.0242767333984375 0.033148871527777776 | |
| 2 0.64794921875 0.5603298611111112 0.039794921875 0.060601128472222224 | |
| 2 0.302490234375 0.9279513888888888 0.1199951171875 0.13802083333333334 | |
| 1 0.56982421875 0.3767361111111111 0.0226287841796875 0.042182074652777776 | |
| 2 0.474609375 0.390625 0.0141143798828125 0.023600260416666668 | |
| 2 0.5439453125 0.3843315972222222 0.01146697998046875 0.017727322048611112 | |
| 2 0.415283203125 0.4032118055555556 0.013275146484375 0.019680447048611112 | |
| 2 0.44677734375 0.3943142361111111 0.0115966796875 0.0184326171875 | |
| 2 0.43701171875 0.3786892361111111 0.0106964111328125 0.018364800347222224 | |