Instructions to use Zeeshanshanih/trafficnet-yolov11-classification_UrbanGuard07 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use Zeeshanshanih/trafficnet-yolov11-classification_UrbanGuard07 with ultralytics:
from ultralytics import YOLOvv11 model = YOLOvv11.from_pretrained("Zeeshanshanih/trafficnet-yolov11-classification_UrbanGuard07") source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
Accident vs Normal Traffic Classifier
Model
YOLOv11 Classification
Classes
- accident
- normal
Dataset
Train
- Accident: 1620
- Normal: 1800
Test
- Accident: 380
- Normal: 400
Total Images: 4200
Training
- Epochs: 50
- Image Size: 224
- Optimizer: AdamW
- Framework: Ultralytics YOLOv11
Files
- best.pt
- results.csv
- results.png
- confusion_matrix.png
- PR_curve.png
- F1_curve.png
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