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
Pin transformers==5.6.2 in install notes
Browse files
README.md
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## Self-contained weights
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This checkpoint includes the full DINOv3 backbone weights in its `safetensors` files. **No external
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DINOv3 weights are downloaded** — the DINOv3 model *structure* is provided by `transformers
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(built-in `dinov3_vit`), and the backbone weights here are derivative weights from full-parameter
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training of DINOv3. The model code (`modeling_page.py`) is loaded automatically from
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[`Octopus1/PaGE`](https://huggingface.co/Octopus1/PaGE) via `auto_map` when you pass
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## Installation
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```bash
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pip install torch torchvision timm "transformers
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```
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Tested with `transformers`
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## Usage
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## Self-contained weights
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This checkpoint includes the full DINOv3 backbone weights in its `safetensors` files. **No external
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DINOv3 weights are downloaded** — the DINOv3 model *structure* is provided by `transformers==5.6.2`
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(built-in `dinov3_vit`), and the backbone weights here are derivative weights from full-parameter
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training of DINOv3. The model code (`modeling_page.py`) is loaded automatically from
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[`Octopus1/PaGE`](https://huggingface.co/Octopus1/PaGE) via `auto_map` when you pass
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## Installation
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```bash
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pip install torch torchvision timm "transformers==5.6.2" safetensors pillow
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```
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Tested with `transformers` 5.6.2.
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## Usage
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