Object Detection
ultralytics
PyTorch
detectionbench
computer-vision
aerial-imagery
drone
uav
vehicle-detection
traffic-surveillance
small-object-detection
Eval Results (legacy)
Instructions to use dronefreak/uavdt-yolo11s with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use dronefreak/uavdt-yolo11s with ultralytics:
from huggingface_hub import hf_hub_download from ultralytics import YOLO # pick the weights file from this repo's "Files and versions" tab weights = hf_hub_download("dronefreak/uavdt-yolo11s", "<weights>.pt") model = YOLO(weights) source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
Download confusion_matrix_normalized.png from dronefreak/uavdt-yolo11s: direct link, hf CLI and curl.
- Browser
- Download file 125 kB
-
https://huggingface.co/dronefreak/uavdt-yolo11s/resolve/main/confusion_matrix_normalized.png
- Command line
-
hf download hf://dronefreak/uavdt-yolo11s/confusion_matrix_normalized.png
-
curl -L -o confusion_matrix_normalized.png https://huggingface.co/dronefreak/uavdt-yolo11s/resolve/main/confusion_matrix_normalized.png
125 kB

- Xet hash:
- 98ec59f304276d47c1efcfba553da3b9507415295e3d614a05d2f06163ae902b
- Size of remote file:
- 125 kB
- SHA256:
- 66bc4821ebe4307845e54086212454223c91cfc1b755427b617535001a6066be
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