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
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# DPT 3.1 (BEiT backbone)
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DPT (Dense Prediction Transformer) model trained on 1.4 million images for monocular depth estimation. It was introduced in the paper [Vision Transformers for Dense Prediction](https://arxiv.org/abs/2103.13413) by Ranftl et al. (2021) and first released in [this repository](https://github.com/isl-org/
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Disclaimer: The team releasing DPT did not write a model card for this model so this model card has been written by the Hugging Face team.
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# DPT 3.1 (BEiT backbone)
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DPT (Dense Prediction Transformer) model trained on 1.4 million images for monocular depth estimation. It was introduced in the paper [Vision Transformers for Dense Prediction](https://arxiv.org/abs/2103.13413) by Ranftl et al. (2021) and first released in [this repository](https://github.com/isl-org/MiDaS/tree/master).
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Disclaimer: The team releasing DPT did not write a model card for this model so this model card has been written by the Hugging Face team.
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