Object Detection
ultralytics
PyTorch
11
ultralyticsplus
yolov11
yolo
vision
visdrone
uav
Eval Results (legacy)
Instructions to use erbayat/yolov11n-visdrone with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use erbayat/yolov11n-visdrone with ultralytics:
from ultralytics import YOLOvv11 model = YOLOvv11.from_pretrained("erbayat/yolov11n-visdrone") source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
| base_model: | |
| - Ultralytics/YOLO11 | |
| tags: | |
| - ultralyticsplus | |
| - yolov11 | |
| - ultralytics | |
| - yolo | |
| - vision | |
| - object-detection | |
| - pytorch | |
| - visdrone | |
| - uav | |
| library_name: ultralytics | |
| library_version: 8.0.239 | |
| model-index: | |
| - name: erbayat/yolo11n-visdrone | |
| results: | |
| - task: | |
| type: object-detection | |
| metrics: | |
| - type: precision | |
| value: 0.34 | |
| name: mAP@0.5(box) | |
| license: openrail | |