Instructions to use Intel/dpt-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Intel/dpt-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("depth-estimation", model="Intel/dpt-large")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForDepthEstimation processor = AutoImageProcessor.from_pretrained("Intel/dpt-large") model = AutoModelForDepthEstimation.from_pretrained("Intel/dpt-large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Intel/dpt-large: direct link, hf CLI and curl.
- Browser
- Download file 1.37 GB
-
https://huggingface.co/Intel/dpt-large/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Intel/dpt-large/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Intel/dpt-large/resolve/main/pytorch_model.bin
1.37 GB
- Xet hash:
- 5654d8c17ab9fee63637bd7ce8979bc618247288afcf18adc2a0f908809715bc
- Size of remote file:
- 1.37 GB
- SHA256:
- 71150941604c39c1c770a72a7b2f56487669f0e3a4d99c8759e233fb1be24080
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