Depth Anything V2 Base

🚀 Depth-Anything-V2-Base

Depth-Anything-V2-Base is a powerful monocular depth estimation model trained with a large amount of synthetic and real-world data, offering high precision and efficiency.

🚀 Quick Start

Depth Anything V2 is trained from 595K synthetic labeled images and 62M+ real unlabeled images, providing the most capable monocular depth estimation (MDE) model.

✨ 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 Examples

Basic 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='vitb', features=128, out_channels=[96, 192, 384, 768])
model.load_state_dict(torch.load('checkpoints/depth_anything_v2_vitb.pth', map_location='cpu'))
model.eval()

raw_img = cv2.imread('your/image/path')
depth = model.infer_image(raw_img) # HxW raw depth map

📄 License

This project is licensed under the CC BY-NC 4.0 license.

📚 Documentation

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}
}