Instructions to use facebook/dpt-dinov2-base-nyu with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/dpt-dinov2-base-nyu with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("depth-estimation", model="facebook/dpt-dinov2-base-nyu")# Load model directly from transformers import AutoImageProcessor, AutoModelForDepthEstimation processor = AutoImageProcessor.from_pretrained("facebook/dpt-dinov2-base-nyu") model = AutoModelForDepthEstimation.from_pretrained("facebook/dpt-dinov2-base-nyu") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 68d5582b310490feabd3e351b75fbc60b5eab70e667f1ac981ac10f06ac06dfb
- Size of remote file:
- 448 MB
- SHA256:
- 6f09db8fd5a5368934ac7eb0ab9881b1fb059d9b85c08ce916ada16f030b22c1
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