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README.md
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@@ -1,13 +1,13 @@
<div align="center">
<h2>Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data</h2>
[**Lihe Yang**](https://liheyoung.github.io/)<sup>1</sup> · [**Bingyi Kang**](https://scholar.google.com/citations?user=NmHgX-wAAAAJ)<sup>2+</sup> · [**Zilong Huang**](http://speedinghzl.github.io/)<sup>2</sup> · [**Xiaogang Xu**](https://xiaogang00.github.io/)<sup>3,4</sup>, [**Jiashi Feng**](https://sites.google.com/site/jshfeng/)<sup>2</sup> · [**Hengshuang Zhao**](https://hszhao.github.io/)<sup>1+</sup>
[**Lihe Yang**](https://liheyoung.github.io/)<sup>1</sup> · [**Bingyi Kang**](https://scholar.google.com/citations?user=NmHgX-wAAAAJ)<sup>2+</sup> · [**Zilong Huang**](http://speedinghzl.github.io/)<sup>2</sup> · [**Xiaogang Xu**](https://xiaogang00.github.io/)<sup>3,4</sup> · [**Jiashi Feng**](https://sites.google.com/site/jshfeng/)<sup>2</sup> · [**Hengshuang Zhao**](https://hszhao.github.io/)<sup>1+</sup>
<sup>1</sup>The University of Hong Kong · <sup>2</sup>TikTok · <sup>3</sup>Zhejiang Lab · <sup>4</sup>Zhejiang University
<sup>+</sup>corresponding authors
<a href=""><img src='https://img.shields.io/badge/arXiv-Depth Anything-red' alt='Paper PDF'></a>
<a href="https://arxiv.org/abs/2401.10891"><img src='https://img.shields.io/badge/arXiv-Depth Anything-red' alt='Paper PDF'></a>
<a href='https://depth-anything.github.io'><img src='https://img.shields.io/badge/Project_Page-Depth Anything-green' alt='Project Page'></a>
<a href='https://huggingface.co/spaces/LiheYoung/Depth-Anything'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue'></a>
@@ -46,7 +46,7 @@ This work presents Depth Anything, a highly practical solution for robust monocu
Here we compare our Depth Anything with the previously best MiDaS v3.1 BEiT<sub>L-512</sub> model.
Please note that the latest MiDaS is also trained on KITTI and NYUv2, while we do not.
Please note that the latest MiDaS is also trained on KITTI and NYUv2, while we are not.
| Method | Params | KITTI || NYUv2 || Sintel || DDAD || ETH3D || DIODE ||
|-|-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|
@@ -99,7 +99,7 @@ python run.py --encoder vitl --load-from checkpoints/depth_anything_vitl14.pth -
If you want to use Depth Anything in our own project, you can simply follow [``run.py``](run.py) to load our models and define data pre-processing.
<details>
<summary>Code snippet (note the difference between our data pre-processing and that of MiDaS.)</summary>
<summary>Code snippet (note the difference between our data pre-processing and that of MiDaS)</summary>
```python
from depth_anything.dpt import DPT_DINOv2
@@ -142,7 +142,7 @@ If you find this project useful, please consider citing:
@article{depthanything,
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},
journal={arXiv:},
journal={arXiv:2401.10891},
year={2024},
}
```

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metric_depth/README.md
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@@ -82,7 +82,7 @@ If you find this project useful, please consider citing:
@article{depthanything,
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},
journal={arXiv:},
journal={arXiv:2401.10891},
year={2024},
}
```