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-21-_png.rf.da4dd60211a46c4e1919f8baa6bbdf1a.txt
| 2 0.54248046875 0.4112413194444444 0.0163421630859375 0.023600260416666668 | |
| 7 0.5569338139910958 0.361865625334316 0.020354941425055274 0.0522694792149567 | |
| 2 0.5263671875 0.3795572916666667 0.0121002197265625 0.017727322048611112 | |
| 2 0.411865234375 0.4574652777777778 0.023773193359375 0.033745659722222224 | |
| 2 0.44189453125 0.48828125 0.027801513671875 0.04226345486111111 | |
| 2 0.42822265625 0.388671875 0.012054443359375 0.018215603298611112 | |
| 7 0.13134765625 0.6371527777777778 0.25732421875 0.390625 | |
| 2 0.440185546875 0.4025607638888889 0.01358795166015625 0.020304361979166668 | |
| 3 0.339599609375 0.5486111111111112 0.058990478515625 0.1106228298611111 | |
| 2 0.3525390625 0.8016493055555556 0.08587646484375 0.13151041666666666 | |
| 1 0.356689453125 0.4403211805555556 0.053985595703125 0.09033203125 | |
| 2 0.654296875 0.7426215277777778 0.07366943359375 0.1291232638888889 | |
| 2 0.48046875 0.3756510416666667 0.0112457275390625 0.016859266493055556 | |
| 2 0.47021484375 0.4064670138888889 0.0147857666015625 0.021267361111111112 | |
| 2 0.46826171875 0.3676215277777778 0.00948333740234375 0.0164794921875 | |
| 2 0.37890625 0.5065104166666666 0.03338623046875 0.04527452256944445 | |
| 2 0.455078125 0.3845486111111111 0.01233673095703125 0.018717447916666668 | |
| 2 0.451904296875 0.3654513888888889 0.0099334716796875 0.013841417100694444 | |
| 2 0.52197265625 0.3606770833333333 0.009124755859375 0.014105902777777778 | |