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 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("HugoHE/m-hood", "<weights>.pt") model = YOLO(weights) source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - YOLOv10
How to use HugoHE/m-hood with YOLOv10:
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("HugoHE/m-hood", "<weights>.pt") model = YOLO(weights) source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
Download faster-rcnn-bdd-vanilla.pth from HugoHE/m-hood: direct link, hf CLI and curl.
- Browser
- Download file 330 MB
-
https://huggingface.co/HugoHE/m-hood/resolve/main/faster-rcnn-bdd-vanilla.pth
- Command line
-
hf download hf://HugoHE/m-hood/faster-rcnn-bdd-vanilla.pth
-
curl -L -o faster-rcnn-bdd-vanilla.pth https://huggingface.co/HugoHE/m-hood/resolve/main/faster-rcnn-bdd-vanilla.pth
330 MB
- Xet hash:
- 1ba778d32396913085a8619376c3d51972dad1effac688e9cf80ff47f1746565
- Size of remote file:
- 330 MB
- SHA256:
- b87e2d543f2b5549477902937cd9ec1a2b492785a31c566bc91789627e377627
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