metadata

library_name: transformers
tags:
  - depth
  - absolute depth
pipeline_tag: depth-estimation

Depth Anything V2 (Fine-tuned for Metric Depth Estimation) - Transformers Version

This model represents a fine-tuned version of Depth Anything V2 for outdoor metric depth estimation using the synthetic Virtual KITTI datasets.

The model checkpoint is compatible with the transformers library.

Depth Anything V2 was introduced in the paper of the same name by Lihe Yang et al. It uses the same architecture as the original Depth Anything release but employs synthetic data and a larger capacity teacher model to achieve much finer and robust depth predictions. This fine-tuned version for metric depth estimation was first released in this repository.

Six metric depth models of three scales for indoor and outdoor scenes, respectively, were released and are available:

Base Model	Params	Indoor (Hypersim)	Outdoor (Virtual KITTI 2)
Depth-Anything-V2-Small	24.8M	Model Card	Model Card
Depth-Anything-V2-Base	97.5M	Model Card	Model Card
Depth-Anything-V2-Large	335.3M	Model Card	Model Card

Model description

Depth Anything V2 leverages the DPT architecture with a DINOv2 backbone.

The model is trained on ~600K synthetic labeled images and ~62 million real unlabeled images, obtaining state-of-the-art results for both relative and absolute depth estimation.

drawing

Depth Anything overview. Taken from the original paper.

Intended uses & limitations

You can use the raw model for tasks like zero-shot depth estimation. See the model hub to look for other versions on a task that interests you.

How to use

Here is how to use this model to perform zero-shot depth estimation:

from transformers import pipeline
from PIL import Image
import requests

# load pipe
pipe = pipeline(task="depth-estimation", model="depth-anything/Depth-Anything-V2-Metric-Outdoor-Small-hf")

# load image
url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
image = Image.open(requests.get(url, stream=True).raw)

# inference
depth = pipe(image)["depth"]

Alternatively, you can use the model and processor classes:

from transformers import AutoImageProcessor, AutoModelForDepthEstimation
import torch
import numpy as np
from PIL import Image
import requests

url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)

image_processor = AutoImageProcessor.from_pretrained("depth-anything/Depth-Anything-V2-Metric-Outdoor-Small-hf")
model = AutoModelForDepthEstimation.from_pretrained("depth-anything/Depth-Anything-V2-Metric-Outdoor-Small-hf")

# prepare image for the model
inputs = image_processor(images=image, return_tensors="pt")

with torch.no_grad():
    outputs = model(**inputs)
    predicted_depth = outputs.predicted_depth

# interpolate to original size
prediction = torch.nn.functional.interpolate(
    predicted_depth.unsqueeze(1),
    size=image.size[::-1],
    mode="bicubic",
    align_corners=False,
)

For more code examples, please refer to the documentation.

Citation

@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}
}