Instructions to use facebook/dino-vitb8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/dino-vitb8 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="facebook/dino-vitb8")# Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("facebook/dino-vitb8") model = AutoModel.from_pretrained("facebook/dino-vitb8") - Notebooks
- Google Colab
- Kaggle
Fixed link typo
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by rosikand - opened
README.md
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# Vision Transformer (base-sized model, patch size 8) trained using DINO
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Vision Transformer (ViT) model trained using the DINO method. It was introduced in the paper [Emerging Properties in Self-Supervised Vision Transformers](https://arxiv.org/abs/
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Disclaimer: The team releasing DINO did not write a model card for this model so this model card has been written by the Hugging Face team.
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# Vision Transformer (base-sized model, patch size 8) trained using DINO
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Vision Transformer (ViT) model trained using the DINO method. It was introduced in the paper [Emerging Properties in Self-Supervised Vision Transformers](https://arxiv.org/abs/2104.14294) by Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, Armand Joulin and first released in [this repository](https://github.com/facebookresearch/dino).
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Disclaimer: The team releasing DINO did not write a model card for this model so this model card has been written by the Hugging Face team.
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