Instructions to use erayyapagci/yolo26n-question-segmentation-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use erayyapagci/yolo26n-question-segmentation-v2 with ultralytics:
# Couldn't find a valid YOLO version tag. # Replace XX with the correct version. from ultralytics import YOLOvXX model = YOLOvXX.from_pretrained("erayyapagci/yolo26n-question-segmentation-v2") source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
Upload model card, weights, and evaluation artifacts
Browse files
README.md
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## Qualitative examples
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The newer YOLO26 checkpoints improve most clearly in two cases:
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## Confidence guidance
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From the confidence sweep used in this release:
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- Best benchmark confidence for YOLO26N is `conf=0.001` (highest mAP50-95 on both held-out test sets).
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- For practical inference, start with `conf=0.25`; use `conf=0.20` if you want fewer missed borderline boxes.
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## Qualitative examples
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The newer YOLO26 checkpoints improve most clearly in two cases:
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