Instructions to use xingyang1/Distill-Any-Depth-Small-hf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use xingyang1/Distill-Any-Depth-Small-hf with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("depth-estimation", model="xingyang1/Distill-Any-Depth-Small-hf")# Load model directly from transformers import AutoImageProcessor, AutoModelForDepthEstimation processor = AutoImageProcessor.from_pretrained("xingyang1/Distill-Any-Depth-Small-hf") model = AutoModelForDepthEstimation.from_pretrained("xingyang1/Distill-Any-Depth-Small-hf") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c3e43844cf392af0d7755345d33216ae874a5ebd934142acc197cc1a3e21623b
- Size of remote file:
- 99.2 MB
- SHA256:
- 6e8233b90eb00ebe71ee6ebba3a7dc1cfe5b5ac0fe11d45069d19bcb4e4ff660
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