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-yolo11n with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use dronefreak/uavdt-yolo11n 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-yolo11n", "<weights>.pt") model = YOLO(weights) source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
Use test-split evaluation plots (PR/F1/P/R curves, confusion matrices) so they match the reported metrics
f95548d verified Download confusion_matrix_normalized.png from dronefreak/uavdt-yolo11n: direct link, hf CLI and curl.
- Browser
- Download file 126 kB
-
https://huggingface.co/dronefreak/uavdt-yolo11n/resolve/main/confusion_matrix_normalized.png
- Command line
-
hf download hf://dronefreak/uavdt-yolo11n/confusion_matrix_normalized.png
-
curl -L -o confusion_matrix_normalized.png https://huggingface.co/dronefreak/uavdt-yolo11n/resolve/main/confusion_matrix_normalized.png
126 kB

- Xet hash:
- 7fff43ee3ce960ca0506593eaa09d066af55a54d730ca98c610b6b6a51850278
- Size of remote file:
- 126 kB
- SHA256:
- c1a066041f482f56b561019a78381d5fc13e438398ffc47411914221c9cb3357
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