Instructions to use Intel/dpt-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Intel/dpt-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("depth-estimation", model="Intel/dpt-large")# Load model directly from transformers import AutoImageProcessor, AutoModelForDepthEstimation processor = AutoImageProcessor.from_pretrained("Intel/dpt-large") model = AutoModelForDepthEstimation.from_pretrained("Intel/dpt-large") - Notebooks
- Google Colab
- Kaggle
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## Model Details: DPT-Large
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Dense Prediction Transformer (DPT) model trained on 1.4 million images for monocular depth estimation.
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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/DPT).
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## Model Details: DPT-Large (also known as MiDaS 3.0)
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Dense Prediction Transformer (DPT) model trained on 1.4 million images for monocular depth estimation.
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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/DPT).
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