Instructions to use hf-internal-testing/tiny-random-DPTForDepthEstimation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-DPTForDepthEstimation with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("depth-estimation", model="hf-internal-testing/tiny-random-DPTForDepthEstimation")# Load model directly from transformers import AutoImageProcessor, AutoModelForDepthEstimation processor = AutoImageProcessor.from_pretrained("hf-internal-testing/tiny-random-DPTForDepthEstimation") model = AutoModelForDepthEstimation.from_pretrained("hf-internal-testing/tiny-random-DPTForDepthEstimation", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1eeec181f47b1034f27f9d935da7f0e31d64f475df9d15d5bba92bd303c007d2
- Size of remote file:
- 71.7 MB
- SHA256:
- 5ab439b636d4bd2b7b702e5ea4ad2e1ccab2416fadb48461894fb3522d6aefd6
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