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
Update readme
Browse files
README.md
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# PaGE: Towards Practical Human-Level Gaze Target Estimation
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PaGE (Practical Gaze Estimator) is a gaze target estimation model that predicts where a person
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is looking in a scene. Gaze target estimation combines high-level understanding of global scene
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semantics with precise spatial reasoning from human appearance (pose, eye orientation). PaGE
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| Model | Backbone | GFLOPs | Weight repo |
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|-------|----------|--------|-------------|
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| PaGE ViT-H+ | DINOv3 ViT-H+
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| PaGE ViT-B Distill | DINOv3 ViT-B | 283.1 | [`Octopus1/page-vitb`](https://huggingface.co/Octopus1/page-vitb) |
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| PaGE ViT-B Distill (Screen) | DINOv3 ViT-B | 283.1 | [`Octopus1/page-vitb-screen`](https://huggingface.co/Octopus1/page-vitb-screen) |
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| PaGE ViT-S+ Distill | DINOv3 ViT-S+
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| PaGE ViT-S Distill | DINOv3 ViT-S | 96.9 | [`Octopus1/page-vits`](https://huggingface.co/Octopus1/page-vits) |
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The ViT-H+ teacher is finetuned end-to-end; the student models are distilled from the teacher via
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author = {Ye, Zhoutong and Zhang, Chengwen and Liu, Jiaqi and Li, Xiangwu and
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Wang, Xutong and Mei, Yu and Cui, Zhaibin and Sun, Mingze and
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Wan, Qingyang and Liu, Chang and Yu, Chun and Shi, Yuanchun},
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year = {2026}
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}
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```
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If you use these models, please also cite the DINOv3 work.
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Project page
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# PaGE: Towards Practical Human-Level Gaze Target Estimation
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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:XXXX.XXXXX](https://arxiv.org/abs/XXXX.XXXXX)
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PaGE (Practical Gaze Estimator) is a gaze target estimation model that predicts where a person
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is looking in a scene. Gaze target estimation combines high-level understanding of global scene
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semantics with precise spatial reasoning from human appearance (pose, eye orientation). PaGE
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| Model | Backbone | GFLOPs | Weight repo |
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|-------|----------|--------|-------------|
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| PaGE ViT-H+ | DINOv3 ViT-H+ | 2373.6 | [`Octopus1/page-vithplus`](https://huggingface.co/Octopus1/page-vithplus) |
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| PaGE ViT-B Distill | DINOv3 ViT-B | 283.1 | [`Octopus1/page-vitb`](https://huggingface.co/Octopus1/page-vitb) |
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| PaGE ViT-B Distill (Screen) | DINOv3 ViT-B | 283.1 | [`Octopus1/page-vitb-screen`](https://huggingface.co/Octopus1/page-vitb-screen) |
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| PaGE ViT-S+ Distill | DINOv3 ViT-S+ | 115.2 | [`Octopus1/page-vitsplus`](https://huggingface.co/Octopus1/page-vitsplus) |
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| PaGE ViT-S Distill | DINOv3 ViT-S | 96.9 | [`Octopus1/page-vits`](https://huggingface.co/Octopus1/page-vits) |
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The ViT-H+ teacher is finetuned end-to-end; the student models are distilled from the teacher via
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author = {Ye, Zhoutong and Zhang, Chengwen and Liu, Jiaqi and Li, Xiangwu and
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Wang, Xutong and Mei, Yu and Cui, Zhaibin and Sun, Mingze and
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Wan, Qingyang and Liu, Chang and Yu, Chun and Shi, Yuanchun},
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year = {2026},
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eprint = {XXXX.XXXXX},
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archivePrefix = {arXiv},
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primaryClass = {cs.CV}
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}
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```
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If you use these models, please also cite the DINOv3 work.
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Project page: [page-26.github.io](https://page-26.github.io/) · Code: [github.com/OctopusWen/PaGE](https://github.com/OctopusWen/PaGE)
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