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
Browse files- .gitattributes +2 -0
- BoxF1_curve.png +2 -2
- BoxPR_curve.png +2 -2
- BoxP_curve.png +3 -0
- BoxR_curve.png +3 -0
- README.md +2 -0
- confusion_matrix.png +2 -2
- confusion_matrix_normalized.png +2 -2
- val_batch0_pred.jpg +2 -2
.gitattributes
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confusion_matrix_normalized.png filter=lfs diff=lfs merge=lfs -text
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uavdt_yolo11n_showcase.jpg filter=lfs diff=lfs merge=lfs -text
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Git LFS Details
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