Depth Anything V2 Small EU AI Act Compliance Profile

Depth-Anything-V2-Small

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

Installation

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

Usage

Download the model first and put it under the checkpoints directory.

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) # HxW raw depth map

Citation

If you find this project useful, please consider citing:

@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}
}

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