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README.md
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See [the Files tab](https://huggingface.co/coreml-projects/depth-anything/tree/main) for converted models.
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## Download
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Install `huggingface-hub`
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See [the Files tab](https://huggingface.co/coreml-projects/depth-anything/tree/main) for converted models.
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Depth Anything model was introduced in the paper [Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data](https://arxiv.org/abs/2401.10891) by Lihe Yang et al. and first released in [this repository](https://github.com/LiheYoung/Depth-Anything).
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[Online demo](https://huggingface.co/spaces/LiheYoung/Depth-Anything) is also provided.
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Disclaimer: The team releasing Depth Anything 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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## Model description
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Depth Anything leverages the [DPT](https://huggingface.co/docs/transformers/model_doc/dpt) architecture with a [DINOv2](https://huggingface.co/docs/transformers/model_doc/dinov2) backbone.
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The model is trained on ~62 million images, obtaining state-of-the-art results for both relative and absolute depth estimation.
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<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/depth_anything_overview.jpg"
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alt="drawing" width="600"/>
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<small> Depth Anything overview. Taken from the <a href="https://arxiv.org/abs/2401.10891">original paper</a>.</small>
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## Download
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Install `huggingface-hub`
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