Instructions to use Intel/dpt-hybrid-midas with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Intel/dpt-hybrid-midas with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("depth-estimation", model="Intel/dpt-hybrid-midas")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForDepthEstimation processor = AutoImageProcessor.from_pretrained("Intel/dpt-hybrid-midas") model = AutoModelForDepthEstimation.from_pretrained("Intel/dpt-hybrid-midas", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Intel/dpt-hybrid-midas: direct link, hf CLI and curl.
- Browser
- Download file 490 MB
-
https://huggingface.co/Intel/dpt-hybrid-midas/resolve/0bf37382252bb097ad697c1df539a37b2e148574/pytorch_model.bin
- Command line
-
hf download hf://Intel/dpt-hybrid-midas@0bf37382252bb097ad697c1df539a37b2e148574/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Intel/dpt-hybrid-midas/resolve/0bf37382252bb097ad697c1df539a37b2e148574/pytorch_model.bin
490 MB
- Xet hash:
- 925926f4eb5ecccadc990a4214ac3cd1f51c9f5b54b89f00010743b44cd1dcdc
- Size of remote file:
- 490 MB
- SHA256:
- 7da362691113a93c3e9ed1e7cc358cfbb890531b10f36fadc695a9949835bc41
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