Instructions to use Intel/dpt-beit-base-384 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Intel/dpt-beit-base-384 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("depth-estimation", model="Intel/dpt-beit-base-384")# Load model directly from transformers import AutoImageProcessor, AutoModelForDepthEstimation processor = AutoImageProcessor.from_pretrained("Intel/dpt-beit-base-384") model = AutoModelForDepthEstimation.from_pretrained("Intel/dpt-beit-base-384") - Notebooks
- Google Colab
- Kaggle
Update README.md (#5)
Browse files- Update README.md (a6875881d41f3442904fc8372537878cc2023386)
README.md
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url = "http://images.cocodataset.org/val2017/000000039769.jpg"
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image = Image.open(requests.get(url, stream=True).raw)
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processor = DPTImageProcessor.from_pretrained("Intel/dpt-
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model = DPTForDepthEstimation.from_pretrained("Intel/dpt-
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# prepare image for the model
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inputs = processor(images=image, return_tensors="pt")
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url = "http://images.cocodataset.org/val2017/000000039769.jpg"
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image = Image.open(requests.get(url, stream=True).raw)
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processor = DPTImageProcessor.from_pretrained("Intel/dpt-beit-base-384")
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model = DPTForDepthEstimation.from_pretrained("Intel/dpt-beit-base-384")
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# prepare image for the model
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inputs = processor(images=image, return_tensors="pt")
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