| | --- |
| | license: cc-by-nc-4.0 |
| | language: |
| | - en |
| | pipeline_tag: depth-estimation |
| | library_name: coreml |
| | tags: |
| | - depth |
| | - relative depth |
| | base_model: |
| | - depth-anything/Depth-Anything-V2-Large |
| | --- |
| | |
| | # Depth Anything V2 Large (mlpackage) |
| |
|
| | In this repo you can find: |
| | * The notebook which was used to convert [depth-anything/Depth-Anything-V2-Large](https://huggingface.co/depth-anything/Depth-Anything-V2-Large) into a CoreML package. |
| | * The mlpackage which can be opened in Xcode and used for Preview and development of macOS and iOS Apps |
| | * Performence and compute unit mapping report for this model as meassured on an iPhone 16 Pro Max and a MacBook Pro (With Apple M3 Pro) |
| |
|
| | As a derivative work of Depth-Anything-V2-Large this port is also under cc-by-nc-4.0 |
| |
|
| |  |
| |
|
| |
|
| | ## 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} |
| | } |
| | |
| | |
