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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.

• 2024-01-22: Paper, project page, code, models, and demo are released.

• Relative depth estimation:

  Our foundation models listed here can provide relative depth estimation for any given image robustly. Please refer here for details.

• Metric depth estimation

  We fine-tune our Depth Anything model with metric depth information from NYUv2 or KITTI. It offers strong capabilities of both in-domain and zero-shot metric depth estimation. Please refer here for details.

• Better depth-conditioned ControlNet

  We re-train a better depth-conditioned ControlNet based on Depth Anything. It offers more precise synthesis than the previous MiDaS-based ControlNet. Please refer here for details.

• Downstream high-level scene understanding

  The Depth Anything encoder can be fine-tuned to downstream high-level perception tasks, e.g., semantic segmentation, 86.2 mIoU on Cityscapes and 59.4 mIoU on ADE20K. Please refer here for details.

Here we compare our Depth Anything with the previously best MiDaS v3.1 BEiT_L-512 model.

Please note that the latest MiDaS is also trained on KITTI and NYUv2, while we do not.

We highlight the best and second best results in bold and italic respectively (better results: AbsRel $\downarrow$ , $\delta_1 \uparrow$).

We provide three models of varying scales for robust relative depth estimation:

• Depth-Anything-ViT-Small (24.8M)

• Depth-Anything-ViT-Base (97.5M)

• Depth-Anything-ViT-Large (335.3M)

You can easily load our pre-trained models by:

from depth_anything.dpt import DepthAnything

encoder = 'vits' # can also be 'vitb' or 'vitl'
depth_anything = DepthAnything.from_pretrained('LiheYoung/depth_anything_{:}14'.format(encoder))

git clone https://github.com/LiheYoung/Depth-Anything
cd Depth-Anything
pip install -r requirements.txt

python run.py --encoder <vits | vitb | vitl> --img-path <img-directory | single-img | txt-file> --outdir <outdir>

For the img-path, you can either 1) point it to an image directory storing all interested images, 2) point it to a single image, or 3) point it to a text file storing all image paths.

For example:

python run.py --encoder vitl --img-path demo_images --outdir depth_visualization

To use our gradio demo locally:

python app.py

You can also try our online demo.

If you want to use Depth Anything in your own project, you can simply follow run.py to load our models and define data pre-processing.

from depth_anything.dpt import DepthAnything
from depth_anything.util.transform import Resize, NormalizeImage, PrepareForNet

import cv2
import torch

encoder = 'vits' # can also be 'vitb' or 'vitl'
depth_anything = DepthAnything.from_pretrained('LiheYoung/depth_anything_{:}14'.format(encoder))

transform = Compose([
    Resize(
        width=518,
        height=518,
        resize_target=False,
        keep_aspect_ratio=True,
        ensure_multiple_of=14,
        resize_method='lower_bound',
        image_interpolation_method=cv2.INTER_CUBIC,
    ),
    NormalizeImage(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
    PrepareForNet(),
])

image = cv2.cvtColor(cv2.imread('your image path'), cv2.COLOR_BGR2RGB) / 255.0
image = transform({'image': image})['image']
image = torch.from_numpy(image).unsqueeze(0)

# depth shape: 1xHxW
depth = depth_anything(image)

If you find this project useful, please consider citing:

@article{depthanything,
      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},
      journal={arXiv:2401.10891},
      year={2024}
}