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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 255106e478017654f851aeeb38d54faa11ece11dd2434f65fcc0b08d13858ceb
- Size of remote file:
- 76.3 MB
- SHA256:
- 227a8c6a6339424ab958c9c6dd1c6791373b0b59f085c2acb8ea35c93915c1f3
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