Instructions to use microsoft/rad-dino with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/rad-dino with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="microsoft/rad-dino")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("microsoft/rad-dino") model = AutoModel.from_pretrained("microsoft/rad-dino", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Use MIT license
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Add files helpful for fine-tuning (#6)
- Update README.md (d59bc916fd05f54ebb9f907085cf619a3f11ab4b) - Add some configs and a module (450a84c7cb080608946574878549592d92c24735) - Add checkpoint compatible with GitHub repo's (6f594db60491d031b4c78786503ac9d8a7bc17e4) - Add weights for DINO head (0f9f28c54f8b6d94e2666e050bffa676c897d59f)428e563
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Verified signature- Add pipeline tag (539a9244ba623e22b0a5913628b7eb645728b6dc) Co-authored-by: Niels Rogge <nielsr@users.noreply.huggingface.co>ed6e71c
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