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:
- c6c912b787a45c486996134e533bba0084be60dc5005f72225084b81b261e5b8
- Size of remote file:
- 3.17 MB
- SHA256:
- 5d0d010a192f969ab87ba61e737ef675ccdf1c97e24a245d873ab95365ffd7d5
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