Instructions to use zenless-lab/vit_base_patch16_dinov3.lvd1689m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use zenless-lab/vit_base_patch16_dinov3.lvd1689m with timm:
import timm model = timm.create_model("hf_hub:zenless-lab/vit_base_patch16_dinov3.lvd1689m", pretrained=True) - Transformers
How to use zenless-lab/vit_base_patch16_dinov3.lvd1689m with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="zenless-lab/vit_base_patch16_dinov3.lvd1689m") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("zenless-lab/vit_base_patch16_dinov3.lvd1689m", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3ed84512f108ff156de7d354937c1a1da65bb57ef1e70a2d1ee82a2381d24eba
- Size of remote file:
- 343 MB
- SHA256:
- e2d07b95f8f0cf09abed22ca86c2c37ab52d8b99ef96e9badd399c36c19ea6c2
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