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)
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
- Xet hash:
- 6bb26a04faab5f66c810b079e8b8533e090b413d6054bdfbfbf4624091afe115
- Size of remote file:
- 166 MB
- SHA256:
- fd7fc0ce7d47e0e09b44a4186ce27e7693873e5b05f89757b0de86fe3f129ccc
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