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-54-_png.rf.70d98fe5106a46fefa5dc84901c94447.txt
| 7 0.4144345238095239 0.37896825396825407 0.02827380952380949 0.062169312169312124 | |
| 2 0.19494047619047628 0.6593915343915346 0.08035714285714286 0.08597883597883602 | |
| 2 0.8177083333333334 0.871693121693122 0.12351190476190484 0.20899470899470915 | |
| 2 0.55517578125 0.4917534722222222 0.03143310546875 0.0478515625 | |
| 1 0.7109375 0.5251736111111112 0.11102294921875 0.16796875 | |
| 2 0.63525390625 0.7239583333333334 0.06927490234375 0.1286892361111111 | |
| 2 0.34521484375 0.4856770833333333 0.03143310546875 0.038492838541666664 | |
| 2 0.4619140625 0.373046875 0.0100555419921875 0.015787760416666668 | |
| 2 0.433349609375 0.5021701388888888 0.03424072265625 0.047037760416666664 | |
| 2 0.4794921875 0.3734809027777778 0.010406494140625 0.014892578125 | |
| 2 0.5341796875 0.4255642361111111 0.01812744140625 0.027113172743055556 | |
| 2 0.587890625 0.4177517361111111 0.015960693359375 0.022623697916666668 | |
| 2 0.448974609375 0.3893229166666667 0.012969970703125 0.01953125 | |
| 2 0.470458984375 0.3940972222222222 0.01433563232421875 0.0201416015625 | |
| 2 0.529296875 0.3997395833333333 0.0127716064453125 0.019680447048611112 | |
| 2 0.456298828125 0.4346788194444444 0.021209716796875 0.029052734375 | |
| 2 0.419921875 0.4396701388888889 0.022796630859375 0.028727213541666668 | |
| 2 0.384033203125 0.4437934027777778 0.0200958251953125 0.029378255208333332 | |
| 2 0.591796875 0.4691840277777778 0.0248870849609375 0.037841796875 | |
| 2 0.564453125 0.4181857638888889 0.0164031982421875 0.025580512152777776 | |
| 2 0.548828125 0.388671875 0.011199951171875 0.016289605034722224 | |
| 2 0.54150390625 0.3734809027777778 0.01061248779296875 0.017388237847222224 | |
| 2 0.451171875 0.3671875 0.008880615234375 0.014336480034722222 | |
| 2 0.56884765625 0.3875868055555556 0.011505126953125 0.020453559027777776 | |