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:
- 8a1b32de6033ac23415150c3c04a8538eb7817cf6029624856db083a42bde6f8
- Size of remote file:
- 76.3 MB
- SHA256:
- 746a123575ccaf12c050845f42b2a76b1fdac47ab1d1c0da6afa4573dab0916a
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