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-24-_png.rf.9073c4d4c49c1b53a0b895868cd9255e.txt
| 3 0.6196302083333334 0.4390185185185185 0.031140624999999922 0.05599074074074082 | |
| 1 0.23156770833333332 0.5677129629629629 0.1431875 0.19303703703703706 | |
| 2 0.33886979166666664 0.49349074074074073 0.03332291666666665 0.040768518518518544 | |
| 2 0.44409374999999995 0.48047222222222213 0.024385416666666694 0.03749074074074075 | |
| 2 0.41528124999999994 0.4461851851851852 0.022890624999999963 0.03174074074074077 | |
| 2 0.5908229166666666 0.46440740740740744 0.022677083333333313 0.03754629629629625 | |
| 2 0.6918958333333334 0.6375833333333334 0.06304687500000003 0.08930555555555565 | |
| 2 0.4653333333333333 0.4136296296296296 0.017578125 0.02360185185185186 | |
| 2 0.6694322916666666 0.5091111111111111 0.03469791666666655 0.051435185185185146 | |
| 2 0.6147447916666666 0.5008703703703704 0.032895833333333256 0.05503703703703704 | |
| 2 0.40942187499999994 0.4121111111111111 0.015604166666666686 0.02360185185185186 | |
| 2 0.53125 0.4047314814814814 0.014333333333333264 0.023509259259259195 | |
| 2 0.5625 0.40581481481481474 0.015015625000000022 0.023055555555555586 | |
| 2 0.5629895833333334 0.3769537037037037 0.01040624999999995 0.015731481481481544 | |
| 2 0.7905260416666666 0.7877592592592592 0.09911979166666667 0.15125925925925937 | |
| 2 0.13134895833333332 0.9375 0.13586458333333332 0.12012037037037039 | |
| 3 0.4411614583333333 0.37065740740740744 0.010984374999999958 0.02053703703703699 | |
| 2 0.43871874999999994 0.4134074074074074 0.017244791666666613 0.022268518518518476 | |
| 2 0.541015625 0.4333796296296296 0.021010416666666743 0.03466666666666656 | |
| 2 0.5864270833333334 0.40234259259259253 0.01616145833333338 0.026083333333333295 | |
| 2 0.5463854166666666 0.38325 0.011244791666666743 0.01952777777777786 | |
| 2 0.4516614583333333 0.38693518518518516 0.013901041666666636 0.02159259259259269 | |