Image Feature Extraction
Transformers
Safetensors
page
feature-extraction
gaze-estimation
gaze-target-estimation
dinov3
custom_code
Instructions to use Octopus1/page-vitb with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Octopus1/page-vitb with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="Octopus1/page-vitb", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Octopus1/page-vitb", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
README: drop venue, note tested transformers versions
Browse files
README.md
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# PaGE ViT-B Distill
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Base distilled student; human-level performance. Part of the [PaGE](https://huggingface.co/Octopus1/PaGE) gaze target estimation family
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(ICLR 2026).
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- **Backbone:** DINOv3 ViT-B (derivative of DINOv3, full-parameter trained)
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- **Params:** ~90M
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pip install torch torchvision timm "transformers>=4.56" safetensors pillow
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```
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## Usage
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```python
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# PaGE ViT-B Distill
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Base distilled student; human-level performance. Part of the [PaGE](https://huggingface.co/Octopus1/PaGE) gaze target estimation family.
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- **Backbone:** DINOv3 ViT-B (derivative of DINOv3, full-parameter trained)
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- **Params:** ~90M
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pip install torch torchvision timm "transformers>=4.56" safetensors pillow
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```
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Tested with `transformers` 4.56.0 and 5.6.2.
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## Usage
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```python
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