Instructions to use Octopus1/PaGE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Octopus1/PaGE with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Octopus1/PaGE", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Browse files
README.md
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**Code:** [github.com/OctopusWen/PaGE](https://github.com/OctopusWen/PaGE) •
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**Project page:** [page-26.github.io](https://page-26.github.io/) •
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**Paper:** [arXiv:
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**Demo:** [page-crossgaze-page.hf.space](https://page-crossgaze-page.hf.space/)
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PaGE (Practical Gaze Estimator) is a gaze target estimation model that predicts where a person
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## BibTeX
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```bibtex
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@misc{
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primaryClass = {cs.CV}
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}
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```
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**Code:** [github.com/OctopusWen/PaGE](https://github.com/OctopusWen/PaGE) •
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**Project page:** [page-26.github.io](https://page-26.github.io/) •
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**Paper:** [arXiv:2607.04860](https://arxiv.org/abs/2607.04860) •
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**Demo:** [page-crossgaze-page.hf.space](https://page-crossgaze-page.hf.space/)
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PaGE (Practical Gaze Estimator) is a gaze target estimation model that predicts where a person
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## BibTeX
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```bibtex
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@misc{ye2026pagepracticalhumanlevelgaze,
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title={PAGE: Towards Practical Human-level Gaze Target Estimation},
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author={Zhoutong Ye and Chengwen Zhang and Zhaibin Cui and Mingze Sun and Jiaqi Liu and Xiangwu Li and Qingyang Wan and Chang Liu and Xutong Wang and Huan-ang Gao and Yu Mei and Chun Yu and Yuanchun Shi},
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year={2026},
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eprint={2607.04860},
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archivePrefix={arXiv},
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primaryClass={cs.CV},
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url={https://arxiv.org/abs/2607.04860},
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}
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```
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