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-31-_png.rf.54634a23196d7f9a19895cbb405c3b1e.txt
| 1 0.37313988095238104 0.42261904761904784 0.045386904761904746 0.08068783068783066 | |
| 2 0.5791015625 0.4444444444444444 0.0225830078125 0.031005859375 | |
| 2 0.451171875 0.4618055555555556 0.0235137939453125 0.03526475694444445 | |
| 2 0.392578125 0.4765625 0.029754638671875 0.04359266493055555 | |
| 2 0.55908203125 0.4034288194444444 0.0147857666015625 0.023695203993055556 | |
| 2 0.453857421875 0.3825954861111111 0.01177978515625 0.018649631076388888 | |
| 2 0.57177734375 0.5269097222222222 0.03662109375 0.056098090277777776 | |
| 2 0.443603515625 0.4016927083333333 0.01507568359375 0.022189670138888888 | |
| 2 0.320068359375 0.5052083333333334 0.03802490234375 0.048394097222222224 | |
| 2 0.4228515625 0.4409722222222222 0.021209716796875 0.03173828125 | |
| 2 0.546875 0.3851996527777778 0.0106964111328125 0.018785264756944444 | |
| 2 0.529296875 0.3914930555555556 0.01322174072265625 0.023600260416666668 | |
| 2 0.53564453125 0.4292534722222222 0.0162200927734375 0.027628580729166668 | |
| 7 0.58349609375 0.3828125 0.028594970703125 0.06928168402777778 | |
| 2 0.6259765625 0.5212673611111112 0.03326416015625 0.054009331597222224 | |
| 2 0.52392578125 0.3667534722222222 0.01078033447265625 0.015909830729166668 | |
| 2 0.356201171875 0.80859375 0.0938720703125 0.15071614583333334 | |
| 2 0.477294921875 0.3576388888888889 0.0074615478515625 0.013841417100694444 | |
| 2 0.5341796875 0.3565538194444444 0.00803375244140625 0.012179904513888888 | |
| 2 0.447265625 0.3678385416666667 0.010009765625 0.013522677951388888 | |
| 2 0.488037109375 0.361328125 0.008575439453125 0.01416015625 | |
| 2 0.546875 0.3524305555555556 0.00661468505859375 0.011711968315972222 | |