Instructions to use erayyapagci/yolo11m-question-segmentation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use erayyapagci/yolo11m-question-segmentation 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("erayyapagci/yolo11m-question-segmentation", "<weights>.pt") model = YOLO(weights) source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
Download ösym_comparison.png from erayyapagci/yolo11m-question-segmentation: direct link, hf CLI and curl.
- Browser
- Download file 947 kB
-
https://huggingface.co/erayyapagci/yolo11m-question-segmentation/resolve/main/%C3%B6sym_comparison.png
- Command line
-
hf download hf://erayyapagci/yolo11m-question-segmentation/ösym_comparison.png
-
curl -L -o ösym_comparison.png https://huggingface.co/erayyapagci/yolo11m-question-segmentation/resolve/main/%C3%B6sym_comparison.png
947 kB

- Xet hash:
- 31031c56e0df0e01ea95251f05bac614a5bcf5f49d4ac4f139dc4f0baf524ea5
- Size of remote file:
- 947 kB
- SHA256:
- 03157830ee066a3d8d2b3b05652cdb559e9c7cdfc654e9a1cc18da005c0f9223
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.