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--- |
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license: apache-2.0 |
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language: |
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- en |
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pipeline_tag: depth-estimation |
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library_name: coreml |
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tags: |
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- depth |
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- relative depth |
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base_model: |
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- depth-anything/Depth-Anything-V2-Small |
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--- |
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# Depth Anything V2 Small (mlpackage) |
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In this repo you can find: |
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* The notebook which was used to convert [depth-anything/Depth-Anything-V2-Small](https://huggingface.co/depth-anything/Depth-Anything-V2-Small) into a CoreML package. |
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* Both mlpackage files which can be opened in Xcode and used for Preview and development of macOS and iOS Apps |
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* Performence and compute unit mapping report for these models as meassured on an iPhone 16 Pro Max |
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* One model uses internal resolution of 518x518 ("Box") and the other 518x392 ("Landscape"). |
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* The "Landscape" is much faster than "Box" but will also give more "juggy" edges, due to the patch I applied to avoid bicubing upsampling (.diff file is also present in this repo) |
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As a derivative work of Depth-Anything-V2-Small this port is also under apache-2.0 |
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## Citation of original work |
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If you find this project useful, please consider citing: |
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```bibtex |
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@article{depth_anything_v2, |
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title={Depth Anything V2}, |
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author={Yang, Lihe and Kang, Bingyi and Huang, Zilong and Zhao, Zhen and Xu, Xiaogang and Feng, Jiashi and Zhao, Hengshuang}, |
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journal={arXiv:2406.09414}, |
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year={2024} |
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} |
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@inproceedings{depth_anything_v1, |
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title={Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data}, |
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author={Yang, Lihe and Kang, Bingyi and Huang, Zilong and Xu, Xiaogang and Feng, Jiashi and Zhao, Hengshuang}, |
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booktitle={CVPR}, |
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year={2024} |
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} |
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