Instructions to use timm/vit_base_patch8_224.dino with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/vit_base_patch8_224.dino with timm:
import timm model = timm.create_model("hf_hub:timm/vit_base_patch8_224.dino", pretrained=True) - Transformers
How to use timm/vit_base_patch8_224.dino with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="timm/vit_base_patch8_224.dino")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("timm/vit_base_patch8_224.dino", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 271c7e86f0d9ed53cc4ab637120931bb3f55196255b18e649daa755ff507bd25
- Size of remote file:
- 343 MB
- SHA256:
- 128c728384bd84a6b9f48e37ec996f02e57da3ca5dad401f3bebe01dbf8e8fa3
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