Object Detection
ultralytics
PyTorch
detectionbench
computer-vision
aerial-imagery
drone
visdrone
detr
roboflow
transformer
Eval Results (legacy)
Instructions to use dronefreak/visdrone-yolo26s with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use dronefreak/visdrone-yolo26s 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/visdrone-yolo26s", "<weights>.pt") model = YOLO(weights) source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
Commit History
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Remove the Earlier VisDrone-DET Results (Companion Codebase) section per request
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Refresh README: Model Zoo table now lists the newest evaluated models; prune stale Earlier Results rows now superseded by DetectionBench's own runs
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Redesign card: usage moved up top, showcase mosaic right under the title, fewer embedded images, leaner citations, shorter showcase mosaic (same template rollout as every other dataset)
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Refresh README: Model Zoo table now lists all 11 evaluated DetectionBench VisDrone models; fix self-referencing URLs to the renamed repo name
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Remove the old showcase GIF (superseded by assets/demo_banner.mp4)
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Update to the DetectionBench 1280 px run: new weights, README, test-split plots and demo banner
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Upload 9 files
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initial commit
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