Instructions to use hf-tiny-model-private/tiny-random-GLPNForDepthEstimation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-GLPNForDepthEstimation with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("depth-estimation", model="hf-tiny-model-private/tiny-random-GLPNForDepthEstimation")# Load model directly from transformers import AutoImageProcessor, AutoModelForDepthEstimation processor = AutoImageProcessor.from_pretrained("hf-tiny-model-private/tiny-random-GLPNForDepthEstimation") model = AutoModelForDepthEstimation.from_pretrained("hf-tiny-model-private/tiny-random-GLPNForDepthEstimation") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 894f0427069e80b976f963e3f6c883e6afb00cdeb957d031717d3961d5b4498c
- Size of remote file:
- 3.11 MB
- SHA256:
- ddfe6d3c4ac3ec61a7d31b89e273659eb7f65723ca93384cf282783554eef38b
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