license: apache-2.0 language:

• en pipeline_tag: depth-estimation library_name: depth-anything-v2 tags:
• depth
• relative depth

Depth-Anything-V2-小型版

简介

Depth Anything V2 基于59.5万张合成标注图像与6200万+真实无标注图像训练而成，是目前性能最强的单目深度估计（MDE）模型，具有以下特性：

• 相比V1版本能捕捉更精细的细节
• 比V1版本及基于SD的模型（如Marigold、Geowizard）更鲁棒
• 比基于SD的模型效率更高（快10倍）且更轻量化
• 使用我们预训练模型进行微调可获得惊艳效果

安装

git clone https://huggingface.co/spaces/depth-anything/Depth-Anything-V2
cd Depth-Anything-V2
pip install -r requirements.txt

使用方法

首先下载模型文件并放入checkpoints目录。

import cv2
import torch

from depth_anything_v2.dpt import DepthAnythingV2

model = DepthAnythingV2(encoder='vits', features=64, out_channels=[48, 96, 192, 384])
model.load_state_dict(torch.load('checkpoints/depth_anything_v2_vits.pth', map_location='cpu'))
model.eval()

raw_img = cv2.imread('your/image/path')
depth = model.infer_image(raw_img) # 输出HxW格式原始深度图

引用

若本项目对您有帮助，请考虑引用：

@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}
}