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Lihe Yang
2024-01-24 23:39:14 +08:00
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@@ -10,6 +10,7 @@
<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>
<a href='https://huggingface.co/papers/2401.10891'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Paper-yellow'></a>
</div>
This work presents Depth Anything, a highly practical solution for robust monocular depth estimation by training on a combination of 1.5M labeled images and **62M+ unlabeled images**.
@@ -191,4 +192,4 @@ If you find this project useful, please consider citing:
journal={arXiv:2401.10891},
year={2024}
}
```
```