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HugoHE
/
m-hood

Object Detection
ultralytics
YOLOv10
PyTorch
computer-vision
faster-rcnn
autonomous-driving
hallucination-mitigation
out-of-distribution
ood-detection
proximal-ood
benchmark-analysis
bdd100k
pascal-voc
Eval Results (legacy)
Model card Files Files and versions
xet
Community
1

Instructions to use HugoHE/m-hood with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • ultralytics

    How to use HugoHE/m-hood with ultralytics:

    from ultralytics import YOLOvv10
    
    model = YOLOvv10.from_pretrained("HugoHE/m-hood")
    source = 'http://images.cocodataset.org/val2017/000000039769.jpg'
    model.predict(source=source, save=True)
  • YOLOv10

    How to use HugoHE/m-hood with YOLOv10:

    from ultralytics import YOLOvv10
    
    model = YOLOvv10.from_pretrained("HugoHE/m-hood")
    source = 'http://images.cocodataset.org/val2017/000000039769.jpg'
    model.predict(source=source, save=True)
  • Notebooks
  • Google Colab
  • Kaggle
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Resources
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  • Code of Conduct
  • Hub documentation

How to correctly use the “balanced dataloader (ID + proximal OoD)” fine-tuning in M-HOOD?

#1 opened 8 months ago by
Suyan01
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