Instructions to use rujutashashikanjoshi/yolo26-vehicle-detection-4139_full-v2_1-100m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use rujutashashikanjoshi/yolo26-vehicle-detection-4139_full-v2_1-100m with ultralytics:
# Couldn't find a valid YOLO version tag. # Replace XX with the correct version. from ultralytics import YOLOvXX model = YOLOvXX.from_pretrained("rujutashashikanjoshi/yolo26-vehicle-detection-4139_full-v2_1-100m") source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
| { | |
| "model_type": "medium", | |
| "architecture": "YOLO26Medium", | |
| "task": "object-detection", | |
| "framework": "ultralytics", | |
| "num_classes": 1, | |
| "class_names": [ | |
| "car" | |
| ], | |
| "epochs": 100, | |
| "batch_size": 16, | |
| "image_size": 640, | |
| "learning_rate": 0.01, | |
| "weight_decay": 0.0005, | |
| "momentum": 0.937, | |
| "warmup_epochs": 3, | |
| "workers": 4, | |
| "patience": 30, | |
| "save_period": 10, | |
| "conf_threshold": 0.25, | |
| "iou_threshold": 0.45, | |
| "output_dir": "./output/4139_v2_1-100m" | |
| } |