metadata

license: cc-by-nc-4.0
language:
  - en
pipeline_tag: depth-estimation
tags:
  - depth
  - relative depth

Depth-Anything-V2_Safetensors

Introduction

Depth Anything V2 is trained from 595K synthetic labeled images and 62M+ real unlabeled images, providing the most capable monocular depth estimation (MDE) model with the following features:

more fine-grained details than Depth Anything V1
more robust than Depth Anything V1 and SD-based models (e.g., Marigold, Geowizard)
more efficient (10x faster) and more lightweight than SD-based models
impressive fine-tuned performance with our pre-trained models
models have been converted into .safetensors

Installation

git clone https://github.com/MackinationsAi/Upgraded-Depth-Anything-V2.git
cd Upgraded-Depth-Anything-V2
one_click_install.bat

Usage

Download the Depth-Anything-V2-Large model | 654.9M | Download | first and put it under the checkpoints directory.
Download the Depth-Anything-V2-Base model | 190.4M | Download | second and put it under the checkpoints directory.
Download the Depth-Anything-V2-Small model | 48.4M | Download | third and put it under the checkpoints directory.
Download the Depth-Anything-V2-Giant model | 1.3B | Coming soon (Still not available) | Download Doesn't Work - Model is still a WIP | fourth and put it under the checkpoints directory.

Citation

If you find this project useful, please consider citing below, give these converted models & upgraded linked repo a star/follow & share it w/ others in the community!

@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}
}