Instructions to use zenless-lab/vit_small_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_small_patch16_dinov3.lvd1689m with timm:
import timm model = timm.create_model("hf_hub:zenless-lab/vit_small_patch16_dinov3.lvd1689m", pretrained=True) - Transformers
How to use zenless-lab/vit_small_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_small_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_small_patch16_dinov3.lvd1689m", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1665f183d74ae09265a488761ff4ce6347a3d83c9caa65a060785da0c10e367d
- Size of remote file:
- 86.4 MB
- SHA256:
- a29ab2fe92714485a416b0a95526baf37561f9a97a48730a621389fc0fe16231
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