| | --- |
| | license: apache-2.0 |
| | language: |
| | - en |
| | pipeline_tag: depth-estimation |
| | library_name: coreml |
| | tags: |
| | - depth |
| | - relative depth |
| | base_model: |
| | - depth-anything/Depth-Anything-V2-Small |
| | --- |
| | |
| | # Depth Anything V2 Small (mlpackage) |
| |
|
| | In this repo you can find: |
| | * 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. |
| | * Both mlpackage files which can be opened in Xcode and used for Preview (of both images and videos) and development of macOS and iOS Apps |
| | * Performence and compute unit mapping report for these models as meassured on an iPhone 16 Pro Max |
| | * One model uses internal resolution of 518x518 ("Box") and the other 518x392 ("Landscape"). |
| | * 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) |
| |
|
| | As a derivative work of Depth-Anything-V2-Small this port is also under apache-2.0 |
| |
|
| |  |
| |
|
| |
|
| | ## Download |
| |
|
| | Install `huggingface-cli` |
| |
|
| | ```bash |
| | brew install huggingface-cli |
| | ``` |
| |
|
| | To download one of the `.mlpackage` folders to the `models` directory: |
| |
|
| | ```bash |
| | huggingface-cli download \ |
| | --local-dir models --local-dir-use-symlinks False \ |
| | LloydAI/DepthAnything_v2-Small-CoreML \ |
| | --include "DepthAnything_v2_Small_518x392_Landscape.mlpackage/*" |
| | ``` |
| |
|
| | ## Citation of original work |
| |
|
| | If you find this project useful, please consider citing: |
| |
|
| | ```bibtex |
| | @article{depth_anything_v2, |
| | title={Depth Anything V2}, |
| | author={Yang, Lihe and Kang, Bingyi and Huang, Zilong and Zhao, Zhen and Xu, Xiaogang and Feng, Jiashi and Zhao, Hengshuang}, |
| | journal={arXiv:2406.09414}, |
| | year={2024} |
| | } |
| | |
| | @inproceedings{depth_anything_v1, |
| | title={Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data}, |
| | author={Yang, Lihe and Kang, Bingyi and Huang, Zilong and Xu, Xiaogang and Feng, Jiashi and Zhao, Hengshuang}, |
| | booktitle={CVPR}, |
| | year={2024} |
| | } |
| | |
| | |
