Instructions to use camenduru/dinov3-vitl16-pretrain-lvd1689m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use camenduru/dinov3-vitl16-pretrain-lvd1689m with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="camenduru/dinov3-vitl16-pretrain-lvd1689m")# Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("camenduru/dinov3-vitl16-pretrain-lvd1689m") model = AutoModel.from_pretrained("camenduru/dinov3-vitl16-pretrain-lvd1689m") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- eef46be2d39b65656610dc9a62e40fcf609d75d8e584a549f5985936496f98b3
- Size of remote file:
- 1.21 GB
- SHA256:
- dcb2e45127cccbf1601e5f42fef165eea275c8e5213197e8dcf3f48822718179
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.