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license: apache-2.0 |
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# Depth Anything Core ML Models |
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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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```bash |
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pip install huggingface-hub |
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``` |
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To download one of the `.mlpackage` folders to the `models` directory: |
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```bash |
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huggingface-cli download \ |
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--local-dir models --local-dir-use-symlinks False \ |
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coreml-projects/depth-anything \ |
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--include "DepthAnythingSmallF16.mlpackage/*" |
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``` |
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To download everything, skip the `--include` argument. |
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