Instructions to use hf-internal-testing/tiny-random-GLPNForDepthEstimation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-GLPNForDepthEstimation with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("depth-estimation", model="hf-internal-testing/tiny-random-GLPNForDepthEstimation")# Load model directly from transformers import AutoImageProcessor, AutoModelForDepthEstimation processor = AutoImageProcessor.from_pretrained("hf-internal-testing/tiny-random-GLPNForDepthEstimation") model = AutoModelForDepthEstimation.from_pretrained("hf-internal-testing/tiny-random-GLPNForDepthEstimation") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a8c36aa8ff1a8f39d110b3aa1678af52088a3c858547de1ae67fb5424c3fd9b3
- Size of remote file:
- 3.11 MB
- SHA256:
- d5d3308d9e895f5472b7cc74ffeb9a20e21445f828eada80b505c4d60fc420ae
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