Image Segmentation
Transformers
PyTorch
English
setcon_chat
feature-extraction
referring-segmentation
video-segmentation
vision-language
custom_code
Instructions to use rookiexiong/SetCon-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rookiexiong/SetCon-8B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="rookiexiong/SetCon-8B", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("rookiexiong/SetCon-8B", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
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SetCon-8B is the model checkpoint for **SetCon: Towards Open-Ended Referring Segmentation via Set-Level Concept Prediction**.
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[\[📂 GitHub\]](https://github.com/rookiexiong7/SetCon)
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[\[📄 Paper\]](https://arxiv.org/abs/2605.
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## Usage
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concepts.
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## Citation
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SetCon-8B is the model checkpoint for **SetCon: Towards Open-Ended Referring Segmentation via Set-Level Concept Prediction**.
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[\[📂 GitHub\]](https://github.com/rookiexiong7/SetCon)
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[\[📄 Paper\]](https://arxiv.org/abs/2605.20110)
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## Usage
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concepts.
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## Citation
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```bibtex
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@article{zhang2026setcon,
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title={SetCon: towards open-ended referring segmentation via set-level concept prediction},
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author={Zhixiong Zhang and Yizhuo Li and Shuangrui Ding and Yuhang Zang and Shengyuan Ding and Long Xing and Yibin Wang and Qiaosheng Zhang and Jiaqi Wang},
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journal={arXiv preprint arXiv:2605.20110},
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year={2026}
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}
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```
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