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
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README.md
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@@ -175,7 +175,7 @@ If you use our models, datasets, or methodology in your research, please cite ou
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For the IROS 2025 conference version, which primarily focuses on YOLO models and represents an earlier conference publication, please cite:
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```
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@inproceedings{
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title={Mitigating Hallucinations in YOLO-based Object Detection Models: A Revisit to Out-of-Distribution Detection},
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author={Weicheng He and Changshun Wu and Chih-Hong Cheng and Xiaowei Huang and Saddek Bensalem},
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booktitle={Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
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```
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For the journal version, which expands the methodology to Faster-RCNN and RT-DETR, includes an automated data curation pipeline, and provides an in-depth analysis of the approach, please cite:
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```
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@inproceedings{
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title={Revisiting Out-of-Distribution Detection in Real-time Object Detection: From Benchmark Pitfalls to a New Mitigation Paradigm},
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author={Changshun Wu and Weicheng He and Chih-Hong Cheng and Xiaowei Huang and Saddek Bensalem},
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year={2025},
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For the IROS 2025 conference version, which primarily focuses on YOLO models and represents an earlier conference publication, please cite:
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```
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@inproceedings{he2025mitigating,
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title={Mitigating Hallucinations in YOLO-based Object Detection Models: A Revisit to Out-of-Distribution Detection},
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author={Weicheng He and Changshun Wu and Chih-Hong Cheng and Xiaowei Huang and Saddek Bensalem},
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booktitle={Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
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```
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For the journal version, which expands the methodology to Faster-RCNN and RT-DETR, includes an automated data curation pipeline, and provides an in-depth analysis of the approach, please cite:
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```
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@inproceedings{wu2025revisiting,
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title={Revisiting Out-of-Distribution Detection in Real-time Object Detection: From Benchmark Pitfalls to a New Mitigation Paradigm},
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author={Changshun Wu and Weicheng He and Chih-Hong Cheng and Xiaowei Huang and Saddek Bensalem},
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year={2025},
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