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license: cc-by-nc-4.0
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---
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license: cc-by-nc-4.0
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language:
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- en
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pipeline_tag: depth-estimation
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tags:
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- depth
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- relative depth
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---
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# Depth-Anything-V2-Base
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## Introduction
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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:
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- more fine-grained details than Depth Anything V1
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- more robust than Depth Anything V1 and SD-based models (e.g., Marigold, Geowizard)
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- more efficient (10x faster) and more lightweight than SD-based models
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- impressive fine-tuned performance with our pre-trained models
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## Installation
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```bash
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git clone https://huggingface.co/spaces/depth-anything/Depth-Anything-V2
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cd Depth-Anything-V2
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pip install -r requirements.txt
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```
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## Usage
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Download the [model](https://huggingface.co/depth-anything/Depth-Anything-V2-Base/resolve/main/depth_anything_v2_vitb.pth?download=true) first and put it under the `checkpoints` directory.
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```python
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import cv2
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import torch
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from depth_anything_v2.dpt import DepthAnythingV2
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model = DepthAnythingV2(encoder='vitb', features=128, out_channels=[96, 192, 384, 768])
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model.load_state_dict(torch.load('checkpoints/depth_anything_v2_vitb.pth', map_location='cpu'))
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model.eval()
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raw_img = cv2.imread('your/image/path')
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depth = model.infer_image(raw_img) # HxW raw depth map
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```
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## Citation
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If you find this project useful, please consider citing:
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```bibtex
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@article{depth_anything_v2,
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title={Depth Anything V2},
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author={Yang, Lihe and Kang, Bingyi and Huang, Zilong and Zhao, Zhen and Xu, Xiaogang and Feng, Jiashi and Zhao, Hengshuang},
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journal={arXiv:2406.09414},
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year={2024}
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}
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@inproceedings{depth_anything_v1,
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title={Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data},
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author={Yang, Lihe and Kang, Bingyi and Huang, Zilong and Xu, Xiaogang and Feng, Jiashi and Zhao, Hengshuang},
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booktitle={CVPR},
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year={2024}
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}
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